I was in this exact situation a couple of times, tasked with developing a complete awareness content strategy for clients in the B2B space. It was a complex project that required deep research, close team coordination, and many hours of manual work. While the manual process was effective, I knew that using AI could significantly increase its speed and efficiency.

Before we dive into the process, it’s important to clarify what a ‘content strategy’ truly is. It’s not just a list of article ideas. A real strategy is the foundational blueprint you create before a single word of content is written. The steps I’m about to outline, **creating personas** to understand who you’re talking to, and **analyzing competitors** to find where you can win, are the most critical parts of this blueprint. This strategic work determines every piece of content that follows.

Here’s a look at how I did it back then, and how AI play a part in it.

## Part 1: The “Then” – The Manual Strategic Process

Our client’s goal was to generate awareness for their heavy machinery and vehicle rental service, targeting other businesses. The audience was niche and as the consultant, my job was to build the content strategic foundation.

### The Manual Approach

1. **Persona Creation:** The first step was understanding the customer. This involved desktop [research on persona](https://blog.hubspot.com/marketing/buyer-persona-research), reading industry forums, and analyzing any initial data the client could provide. I pieced together profiles of procurement managers and project leads, trying to understand their daily challenges and priorities. This process took quite awhile of synthesis to produce just two or three solid personas.

2. **Competitor Analysis:** Next, I had to map out the competitive landscape. I spent significant time, discussion with the sales team of the company (client visit also useful to gain additional insight), manually searching Google and using tools like [Ahrefs](https://ahrefs.com/) to identify competitors. I would visit their websites, read their blog posts, and analyze their backlink profiles to understand their strategy. It was a time-consuming process of detective work to figure out their positioning and value proposition.

3. **Agency and Team Coordination:** With a strategy taking shape, I had to coordinate with other teams. I worked with the social media team to ensure our messaging was human and approachable, making it easy for potential clients to connect with us. I also briefed the creative team on the content direction. This involved detailed meetings and documents to ensure everyone was aligned.

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This manual process worked. It was thorough and grounded in real analysis. But it was slow. Each step was a significant time investment, delaying the move from strategy to execution.

## Part 2: The “Added” – The AI-Enhanced Workflow

With the Added AI workflow, my core strategic thinking wouldn’t change, but it would be dramatically different in speed and efficiency. AI tools would act as a powerful assistant, supercharging each step of the process.

### The AI-Assisted Approach

1. **AI for Rapid Persona Development:** Instead of spending hours or even days on research, I’d start with an AI model like Gemini or Claude. I’d feed it market research reports, industry articles, and my own initial analysis. A prompt could look something like this:

* *”Act as a B2B marketing strategist. Based on the attached market data for the Indonesian construction industry, draft four detailed buyer personas for a heavy machinery rental service. Include their job titles, primary goals, daily pain points, and where they look for information online.”*

The AI would generate detailed draft personas in second. The key here is that these are *drafts*. I would then validate and refine them using the client’s actual customer list and my own expertise. This is not about replacing judgment, but about automating the initial heavy lifting.

2. **AI for Deep Competitor Analysis:** Manually tracking competitors is grueling. Today, I’d use an AI-powered research tool like Perplexity, or even the advanced search capabilities in Gemini. These tools can synthesize information from across the web in seconds. My goal is to get a quick, accurate landscape view. For truly ground-level insights, I might use Grok to tap into real-time conversations on X (Twitter).

It’s important to remember that [AI can sometimes “hallucinate” or invent facts](https://www.techtarget.com/whatis/definition/AI-hallucination). Because of this, cross-checking the information is a mandatory step. I would use the AI’s output as a starting point, a map that tells me where to look. From there, I’d dive into the primary sources to verify the key insights. This approach helps in framing [From Keywords to Context: Preparing Your Content for Google’s AI-Powered Search](/from-keywords-to-context-preparing-your-content-for-googles-ai-powered-search/), ensuring the strategy is built on a solid foundation.

3. **AI for Creative Briefing and Coordination:** Briefing a creative agency requires clear communication. Instead of just writing a document, I’d use AI image generation tools to create a visual mood board. I could generate examples of the photographic style, color palettes, and overall aesthetic I’m imagining for the brand. This gives the creative team a much stronger, more tangible starting point for their work. The final decision still rests with human designers, whose insights are invaluable, but the process of getting there becomes much faster. This is similar to the challenge I discussed in **[“The B2B Keyword Mess,”](/ai-in-marketing-seo/the-b2b-keyword-mess-how-i-cleaned-it-manually-and-how-to-do-it-faster-with-ai-today/)** where AI helps clear the initial clutter so you can focus on the important details.

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### Key Takeaways

* **AI Accelerates, Not Replaces:** The core principles of B2B strategy remain the same. AI tools just help you get through the foundational research and data processing stages much faster.
* **Human Validation is Crucial:** AI-generated outputs like personas and competitor analyses are first drafts, not final products. They must be checked and refined with human expertise and real-world data.
* **Focus on Higher-Level Thinking:** By automating the manual work, AI frees up the strategist to focus on what truly matters: interpreting the data, identifying unique opportunities, and making the final strategic decisions.

In the end, the goal is to build a winning strategy. My experience has shown me that while the tools may change, the strategic thinking behind them is what drives success. Today’s AI simply allows us to apply that thinking more quickly and effectively than ever before.

## Frequently Asked Questions (FAQ)

**Q: Won’t using AI for personas make them generic and less accurate?**
**A:** They can be, but only if you accept the first draft without any refinement. The key is to use AI for speed, not for the final answer. You should provide the AI with your specific market data, and then take the draft it produces and validate it against actual client interviews, sales data, and your own industry knowledge. Think of it as a starting point, not the finish line.

**Q: What’s the biggest mistake people make when using AI for competitor analysis?**
**A:** The biggest mistake is trusting the AI’s output blindly. AI models can “hallucinate” or misinterpret data. You must treat an AI-generated summary as a well-organized research brief, not as absolute fact. Always click on the sources or conduct your own searches to verify key claims before building them into your strategy.

**Q: How can I convince my boss or client to approve using AI in our process?**
**A:** Focus on efficiency and better outcomes. Frame it as a way to deliver a more comprehensive strategy in less time. You could explain, “By using AI to automate the initial research, I can spend more time analyzing the competitive landscape and identifying an innovative market position for us.” Showing the “then vs. now” workflow makes the benefits tangible.

**Q: What is a simple first step to integrate AI into my current workflow?**
**A:** Start small and simple. The next time you have to research a new topic, ask an AI research tool like Perplexity in addition to your standard Google search, then compare the results. Or, when you need to write a difficult email, ask Gemini or Claude to draft a version for you. Seeing its utility in small, daily tasks builds confidence for larger strategic work.

**Q: How do you manage team collaboration when AI is involved in the strategy phase?**
**A:** Transparency is essential. Clearly define where AI has been used (for example, “This persona is an AI-generated draft for our discussion”). Use AI-generated materials, like mood boards or research summaries, as shared starting points for brainstorming sessions. This ensures the team views AI as a tool to kickstart collaboration, not a black box that dictates the final answer.

The post B2B Awareness Content Strategy Example: Manual Plan Boosted With AI-Enhanced Workflow appeared first on Hardian Nazief.

Back then, fixing this meant rolling up my sleeves and diving into spreadsheets. It was a slow, manual process of trying to separate the wheat from the chaff. Today, the game has completely changed. With AI, I could solve that same problem in a fraction of the time and with much greater accuracy. If you’re an aspiring marketer trying to get the most out of your ad budget, understanding how to use AI for keyword analysis is no longer a luxury, it’s essential.

### The Grind: My Manual B2B Keyword Separation Process

I remember the painstaking process clearly. It wasn’t just a simple task; it was a multi-layered strategy executed almost entirely by hand, involving both pre-click guesswork and post-click analysis.

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First, we had our **Pre-Click Strategy**, which was our hypothesis about which keywords would work. This involved several manual steps:

1. **Modifier-Based Filtering:** I would export our keyword lists from Google Ads into Excel and begin by creating extensive lists of negative keywords. This was our first line of defense to filter out clear consumer traffic using terms like “DIY,” “for home,” “repair,” and “tutorial.”

2. **Semantic Guesswork and Pattern Matching:** This was the most challenging part. I made educated guesses based on keyword phrasing. A search for `”ball bearing”` is broad, but `”distributor SKF 6204 bearing”` signals a clear B2B intent. We would manually tag these as high-priority and increase our bids.

3. **Specificity Analysis:** We operated on the assumption that highly specific, long-tail keywords, even with lower search volume, came from professionals who knew exactly what they needed. We spent hours identifying and prioritizing these high-specificity terms.

4. **Geographic Budgeting:** Since we were in Indonesia, we knew our most valuable clients were in major industrial hubs. A key part of our strategy was manually setting our ad campaigns to push the budget more aggressively in cities like Cikarang, Surabaya, and industrial areas around Jakarta.

But my work didn’t stop there. After paying for the click, I had to move on to Post-Click Validation to see if my hypotheses were correct. This is where I lived in Google Analytics, constantly monitoring two key metrics:

* **Bounce Rate:** If a specific keyword drove a lot of clicks but had a 80% bounce rate, it was a red flag. It told us the user landed on our B2B page and immediately left, confirming a B2C intent. This data was our trigger to add that term to our negative keyword list.
* **Average Time on Site:** A low time on site was another clear signal. An engineer or procurement manager would spend time reviewing specs. A wrong targeted audience would not. This metric helped us confirm the quality of the traffic from our chosen keywords.

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This created a laborious, reactive cycle: we’d spend money on clicks, wait for the post-click data to come in, analyze it, and then go back to manually refine our keyword lists and location targeting. It was a constant grind of filtering and validating, and it left very little room for high-level strategic thinking.

While there are many things that I didnt mention here, this was just a snapshot of the constant, manual calibration required, a process that was effective but incredibly time-consuming.

### The Game Changer: Re-Analyzing the Problem with an AI Lens

Today, I wouldn’t start with a spreadsheet. I’d start with AI. The difference is that AI doesn’t just match keywords; it understands context and, most importantly, *user intent*. This is the core principle I’ve discussed before in my post about [how AI is changing day-to-day SEO tasks](https://hardiannazief.com/ai-in-marketing-seo/my-take-how-ai-is-really-changing-day-to-day-seo-tasks-beyond-the-hype/). Instead of just asking “what” a user is searching for, AI helps us understand “why.”

Is the user in research mode? Are they comparing prices? Or are they ready to make a purchase? Answering these questions is the key to separating B2B from B2C traffic effectively. AI tools can analyze thousands of data points from the search engine results page (SERP) to determine this intent, moving us from a keyword-focused approach to a context-focused one.

### My AI Toolkit for Keyword Intent Analysis

If I were tasked with that same challenge today, here’s the toolkit I would use to get it done faster and better.

#### **1. AI-Powered SEO Platforms ([Ahrefs](https://ahrefs.com/), [Semrush](https://www.semrush.com/))**

Modern SEO tools have already integrated AI to help with this exact problem. Instead of guessing, you can use their built-in features to filter keywords by user intent (Informational, Navigational, Commercial, Transactional). This allows you to immediately see which keywords are signaling a “ready to buy” mentality, which is often where B2B clients live. You can quickly identify and focus on the keywords that lead to conversions.

#### **2. Using LLMs as Your Personal Data Analyst (ChatGPT, Gemini, Claude)**

This is one of the most powerful and accessible methods. You can take a list of your keywords, paste them into a Large Language Model (LLM) like ChatGPT or Gemini, and ask it to analyze them for you.

Here is a prompt you can use:

> “I am running a Google Ads campaign for a B2B company that sells industrial machine parts in bulk. I need to separate my keywords between those with B2B intent (e.g., procurement managers, engineers) and B2C intent (e.g., hobbyists, DIY users). Please analyze the following list of keywords and categorize them into ‘Likely B2B’, ‘Likely B2C’, and ‘Ambiguous’. For each keyword, briefly explain your reasoning.”

This turns the AI into a junior analyst who can do the heavy lifting in seconds, not days.

#### **3. Learning Directly from the SERP**

The best source of truth for user intent is Google itself. Tools like [Clearscope](https://www.clearscope.io/) or [SurferSEO](https://surferseo.com/) analyze the top-ranking pages for a given keyword. Are the top results blog posts and guides? The intent is likely informational (B2C or early-stage B2B). Are they product pages and pricing lists from major distributors? The intent is likely transactional and commercial (prime B2B territory). Analyzing the SERP is crucial as we continue the shift [from keywords to context in Google’s AI-powered search](https://hardiannazief.com/ai-in-marketing-seo/from-keywords-to-context-preparing-your-content-for-googles-ai-powered-search/).

### The Strategy Still the Same but Amplified by AI

Looking back at my time manually sorting keywords, the goal was always to spend our budget more effectively. That hasn’t changed. What has changed are the tools at our disposal. The manual grind taught me the fundamentals of keyword analysis, but AI provides the speed and sophistication to apply those fundamentals at scale.

AI doesn’t replace the strategist. It augmented you. By handing off the tedious, manual tasks to AI, you free up your time to focus on what truly matters: understanding your customer, crafting a compelling message, and making high-level decisions that drive growth. It’s the difference between being a mechanic and being an engineer, and it’s how modern marketers will succeed.

—

### **Key Takeaways**

* High Cost Per Acquisition (CPA) in B2B marketing is often caused by attracting B2C clicks with the wrong keywords.
* The manual process of separating B2B and B2C keywords is slow, inefficient, and relies on guesswork.
* AI changes the game by analyzing user intent and semantics, not just keyword strings.
* You can use AI-powered SEO tools like Ahrefs or an LLM like ChatGPT to categorize keywords by intent quickly.
* Analyzing the Search Engine Results Page (SERP) gives clear signals about whether user intent is informational or transactional.

### **Frequently Asked Questions (FAQ)**

**Q: Can AI completely replace human oversight for keyword research?**
A: No. AI is a powerful tool for analysis and categorization, but a human strategist is still needed to understand the business context, the nuances of the target audience, and to make the final strategic decisions.

**Q: Can AI completely replace keyword research tools like Ahrefs?**
A: No, not at all. Think of AI models like ChatGPT or Gemini as powerful analysts, but they don’t have the live pipeline of search data that tools like Ahrefs and Semrush do. The best workflow uses both: the SEO platform to get the raw data and initial clusters, and the LLM for deeper, more nuanced analysis of your lists.

**Q: Is it expensive to use AI for keyword analysis?**
A: Not necessarily. While premium SEO platforms have a cost, using LLMs like the free versions of ChatGPT or Gemini can provide powerful analysis for a list of keywords at no cost.

**Q: How do I know if a keyword has B2B or B2C intent?**
A: Look for signals. B2B keywords often include terms like “bulk,” “supplier,” “distributor,” “enterprise,” or specific model numbers. B2C keywords might include “DIY,” “how-to,” “for home,” or “cheap.” AI can help you spot these patterns much faster.

**Q: How can I trust the AI’s B2B/B2C classification?**
A: Always use it as a powerful assistant, not a final decider. The prompt I provided gives the AI a clear role and criteria, which leads to highly accurate results. However, you should always do a final review. You know your business and customer better than anyone, and your final strategic oversight is what makes the process work.

The post The B2B Keyword Mess: How I Cleaned It Manually, and How To Do It Faster with AI Today appeared first on Hardian Nazief.

In the B2B customer lifecycle, every piece of content should have a purpose. As the diagram below shows, activities like SEO and Content Marketing are critical for the very first stage: Reach. This is where you generate demand and capture purchase intent before a prospect even knows they need you. The content you create here is your first handshake.

The problem is, many people turn to AI for a quick fix. They prompt it to “write a blog post about industry X” and get a generic article that fails to connect. For the crucial ‘Reach’ stage, this soulless content doesn’t build authority or trust, and it won’t attract the right audience.

This article lays out a repeatable workflow specifically for creating high-performance blog content for the Reach stage. It’s a professional process that treats AI as a strategic assistant to enhance, not replace, your expertise.

Key Takeaways

• B2B content should be tailored to a specific lifecycle stage, blog posts are key for ‘Reach’.
• Generic AI prompts create ineffective content for attracting new, problem-aware audiences.
• A structured workflow puts you in control, using AI as a research and drafting assistant.
• The most critical step is adding your unique human expertise and personal stories to the draft.

The Core Workflow for ‘Reach’ Stage Content

Step 1: Build the Brief by Developing a ‘Reach’ Stage Persona

Before writing, you must know who you’re trying to attract. For the ‘Reach’ stage, your goal is to connect with people who are problem-aware but might not be solution-aware. Your reader persona, a semi-fictional representation of this ideal customer, it must reflect this reality.

Use an AI tool as a research analyst to gather the raw information for your persona.

The Prompt to Use:

“Act as a B2B marketing strategist. I am creating a blog post for the ‘Reach’ stage of the customer lifecycle. My topic is ‘CRM implementation for small sales teams’. My target reader is a ‘Sales Manager at a tech startup with 10-20 employees’ who is just starting to research their options. For this persona, identify their top 3 initial pain points, top 3 desired outcomes, and 2-3 common questions they would ask at the beginning of their search.”

This prompt gives you the building blocks for a persona who is right at the beginning of their journey. You then structure this into a clear document.

Here is an example persona for the ‘Reach’ stage:

Now, you’re writing directly to Sari, who is in discovery mode. Your content will meet her exactly where she is.

Step 2: Craft a Strategic, SEO-Informed Outline

With a clear ‘Reach’ stage persona like Sari, your outline becomes a magnet for search traffic from people like her. It directly answers her early-stage questions. This is how you move From Keywords to Context, which is vital for SEO success.

A strong, self-contained prompt gives the AI all the context it needs to produce a high-quality outline.

The Prompt to Use:

*”Based on the following persona, create a detailed blog post outline.
Persona Details:
* Role: Sales Manager at a Tech Startup (10-20 Employees)
* Stage: Reach / Problem-Aware
* Pain Points: Leads falling through cracks, no single source of truth, team hates admin work.
* Early Questions: “Do we need a tool for this?”, “What are the signs we’ve outgrown spreadsheets?”

Instructions:
* The primary keyword is ‘signs you need a CRM’.
* The outline must focus on her pain points and directly answer her early questions.
* Structure it as a listicle titled ‘5 Signs Your Sales Team Has Outgrown Spreadsheets’.
* Use H2s for each sign and H3s for supporting points.
* Include a concluding section on how to justify the next steps to a manager.
* Include a FAQ section to address her specific questions.”*

An AI-powered assistant, given this detailed prompt, can generate a highly relevant and structured outline that resonates with modern readers.

Example of the Generated Outline:
* H1: 5 Signs Your Sales Team Has Outgrown Spreadsheets
* H2: Introduction: That nagging feeling that your process is about to break.
* (Hooks Sari’s current frustration and validates her feelings).
* H2: Sign 1: Important Leads Are Falling Through the Cracks
* H3: The real cost of a single missed follow-up.
* H3: Why manual tracking can’t keep up with growth.
* H2: Sign 2: Your Customer Data is a Mess
* H3: The danger of not having a ‘single source of truth’.
* H3: How inconsistent data leads to embarrassing customer interactions.
* H2: Sign 3: Your Best Sales Reps Are Drowning in Admin Work
* H3: Calculating the time spent on data entry vs. actual selling.
* H2: Sign 4: You Have Zero Visibility into Your Sales Pipeline
* H3: Why you can’t forecast accurately with spreadsheets.
* H3: Answering the simple question: “How are we tracking towards our goal?”
* H2: Sign 5: You Can’t Personalize Your Outreach at Scale
* H2: What’s Next? How to Justify Exploring a CRM to Your CEO
* (Directly addresses Sari’s goal of justifying further research).
* H2: Frequently Asked Questions
* H3: Are CRMs too expensive for a small startup?
* H3: Will my team actually use it?

Step 3: Execute a Controlled, Section-by-Section Draft

Drafting section by section gives you maximum control. Prompt the AI to write one H2 section at a time, always keeping Sari’s ‘Reach’ stage mindset at the forefront.

The Prompt to Use:

“Write the introduction for this blog post. Start with a hook that Sari can relate to, like the frustration of losing an important lead in a messy spreadsheet. Frame the article as a guide to help her diagnose her team’s workflow problems. Keep the tone helpful and educational, not salesy.”

Step 4: The Crucial Human Touch — Adding Your Expertise

This transforms the draft into an authoritative ‘Reach’ stage asset. This is your work, not the AI’s.

• Inject Personal Stories & Data: This builds immediate trust. For example, “I remember managing marketing for a B2B client whose sales team was exactly in Sari’s position. Once we created top-of-funnel content that helped them simply identify their problems, our organic traffic grew significantly. It was a key part of the strategy that helped increase organic traffic by 151% because we were helping, not selling.”
• Refine the Nuance: Edit the AI’s text to match your voice. Add your unique perspective on the problem.
• Check for Factual Accuracy: Verify all claims.
• Add Custom Visuals: Use your own screenshots or diagrams. This personal touch is impossible for AI to replicate.

Why This Workflow Is Better

1. Focused on ‘Reach’ Goals: This process is tailored to generate demand and attract new prospects by providing value upfront—the exact purpose of the ‘Reach’ stage.
2. Quality & Relevance: It starts with a specific customer persona and their problems, ensuring the final piece is genuinely helpful for someone at the top of the funnel.
3. Control & Authenticity: It keeps you, the expert, in the driver’s seat, ensuring the final product reflects your authority.
4. Strategic SEO: It bakes search strategy into the process from the beginning.

Conclusion

The goal of AI isn’t to automate content creation, but to augment your strategic process. This workflow allows you to scale the production of high-quality, top-of-funnel content that attracts the right audience and fills your pipeline. True value comes from combining a smart process with your irreplaceable human expertise.

Frequently Asked Questions (FAQ)

• Q: Will this workflow take longer than just asking an AI to write the whole thing?
  • A: Yes, but it produces a high-quality asset that will perform its ‘Reach’ function effectively, saving you from having to rewrite a generic article that gets no traffic.
• Q: Can you use a similar process for other stages like ‘Act’ or ‘Convert’?
  • A: Absolutely. You would simply create a different persona for that stage (e.g., someone who is now comparing vendors) and adjust your prompts to address their specific goals and questions.

The post From Blank Page to B2B Blog Post: A Strategic Content Workflow for the ‘Reach’ Stage appeared first on Hardian Nazief.

The core of the issue is getting your attribution right. It’s how you prove your Return on Investment (ROI) and make sharp decisions to grow a business. Being able to explain this basic fundamental is how you demonstrate your impact and justify your budget.

### The Core Difference: How Analytics Separates Traffic

The primary way to distinguish between traffic sources is by using a web analytics platform. The industry standard is [**Google Analytics (GA4)**](https://analytics.google.com/). The distinction is simple.

– **Paid Ads (SEM/PPC):** When you run a campaign on Google Ads, the platform adds a special parameter to your website URL every time someone clicks your ad. This tag, called a `gclid`, is like a digital passport. When a visitor arrives at your site with this passport, Google Analytics reads it and says, “Aha, this one came from a paid ad.” It then sorts this visitor into the `Paid Search` channel.
– **Organic Search (SEO):** This is the traffic you *earn* when someone searches for a term and clicks on your website in the unpaid results. These visitors arrive without that special `gclid` passport. Analytics sees they came from a search engine like Google but don’t have the paid tag, so it knows this traffic is organic and files it under the `Organic Search` channel.

At my previous professional role, this distinction was critical. We had a significant budget for Google Ads but were also investing heavily in SEO. Our ability to separate these channels in our reports was the only way to show management that while paid ads brought immediate results, our SEO strategy was building a powerful, long-term asset that steadily grew our organic traffic significantly.

### Process for Connecting Leads to SEO

Knowing where traffic comes from is just the first step. You need to connect that traffic to an actual conversion. Here is the approach.

**First, you must decide what a “lead” is for your business.**
It could be any number of user actions:

– A completed contact form.
– Filling out a “Request a Quote” form.
– A phone number click on a mobile device.
– A subscription to your newsletter.
– A download of a guide or case study.

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Once we define what a lead is, these set up as **Key Events** in Google Analytics 4. For instance, I’ll configure a custom event like “generate_lead” to fire every time a user successfully submits the main contact form. This turns a simple action into a measurable goal.

**Then, find the proof in GA4.**
With your Key Events set up. Head straight to the `Reports > Acquisition > Traffic acquisition` report in Google Analytics. This is the source of truth. The report lays it all out, showing your metrics by channel. You just find the `Organic Search` row, look across to your “Key Events” column, and boom, there’s your number. **That’s how many leads your SEO work generated.**.

**Finally, make it easy to see.**
No one has time to dig through analytics reports all day. I connect my Google Analytics and Google Search Console data to a [**Looker Studio**](https://lookerstudio.google.com/) dashboard. This creates a simple, visual, and automated report that shows our lead numbers broken down by channel (SEO vs. Paid Ads vs. others). I can create a single, clear view that shows:

– Total leads generated month-over-month.
– A pie chart breaking down leads by channel (Organic Search, Paid Search, Direct, etc.).
– The Cost Per Acquisition (CPA) for paid channels compared to organic.

This visual reporting makes it incredibly easy for stakeholders to see the value of SEO. At my previous roles, this was the kind of data-driven reporting management loved because it directly connected our keyword optimization work to lead quality and cost savings.

### Don’t Forget: The Website Must Be Ready to Convert

Getting traffic from SEO is great, but if your website isn’t optimized to capture leads, all that effort goes to waste. A deep [**understanding of user intent is the cornerstone of effective SEO in the AI era**](https://hardiannazief.com/ai-in-marketing-seo/understanding-user-intent-cornerstone-of-effective-seo-in-the-ai-era/), because it guides you in giving users exactly what they need on the page.

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Here are the primary methods I’ve used to capture leads on-site:

1. **Optimized Contact Forms:** Place clear, simple forms on key pages. The fewer fields required, the higher the conversion rate.
2. **Compelling Calls-to-Action (CTAs):** Use strong, action-oriented buttons like “Get a Free Quote” or “Download Our Guide.”
3. **Gated Content:** Offer a valuable resource, like an ebook or case study, in exchange for an email address. This is very effective for B2B.
4. **Click-to-Call Buttons:** On mobile, your phone number should always be a clickable link. These clicks can be tracked as conversion events.
5. **Live Chat / Chatbots:** Engage visitors in real-time to answer questions and collect contact information.

But here’s a crucial piece of advice: **what works in one country can totally fail in another.** Take my experience in Indonesia. The standard B2B “Schedule a Consultation” or “Get a Free Quote” buttons? They don’t perform well. B2B clients here would much rather schedule an in-person visit or just call someone directly to talk details. What *did* work wonders? Offering a small, tangible gift, like a Bluetooth speaker or a company mug, for filling out a form. And don’t get me started on AI chatbots. People here can spot them a mile away and will leave the site instantly if they feel they’re not talking to a real person.

CTAs offering a tangible incentive like a Bluetooth speaker, a flash drive, or a company mug; have proven to be the most effective at generating leads. This local preference extends to technology; there’s an immense dislike for AI chatbots, and users will often leave a site immediately if they realize they aren’t speaking to a human. This underscores a crucial point: you must test what works for your specific audience, as universal “best practices” often fail when they ignore local culture and behavior.

### A Final Thought: Things Are Always Changing

With Google’s latest updates rolling out AI Overviews, we can expect to see shifts in traffic patterns. You can read more about on [preparing for Google’s AI-Powered Search](https://hardiannazief.com/ai-in-marketing-seo/from-keywords-to-context-preparing-your-content-for-googles-ai-powered-search/). However, the core principles of attribution remain the same. No matter how users find you, your job is to set up your analytics correctly, track what matters, and report on it clearly.

### Fast Facts

– **Organic vs. Paid:** Google Analytics automatically separates traffic from paid ads (SEM) and organic search (SEO).
– **Track Everything:** Define leads (e.g., form fills) as Key Events in GA4 to measure what matters.
– **Report Visually:** Use Looker Studio to create automated dashboards that clearly show SEO ROI.
– **Optimize for Capture:** Use strong CTAs, simple forms, and other methods to convert traffic into leads.
– **Report for Clarity:** Use Looker Studio to visualize your channel performance so everyone understands the data.

### Frequently Asked Questions (FAQ)

**How do I know for sure if a lead is from SEO?**
If you have set up Key Events (conversions) in Google Analytics 4, the Traffic Acquisition report will show you the exact number of conversions attributed to the “Organic Search” channel. This is your most reliable source of truth.

**What is the best tool to track marketing leads?**
Google Analytics 4 is the essential free tool for tracking where your leads come from. For a deeper understanding of which keywords are driving traffic, you also need Google Search Console. For competitive analysis and page-level insights, I use tools like Ahrefs or Semrush.

**Why is lead attribution so important?**
Lead attribution is crucial because it allows you to prove the Return on Investment (ROI) of your marketing efforts. It shows you which channels are working best, so you can optimize your budget and strategy for better results.

The post SEO & Paid Ads: How to Tell Where Your Leads Come From? appeared first on Hardian Nazief.

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As I’ve discussed before in my guide to [Practical AI Prompting](https://hardiannazief.com/ai-for-writing-content-creation/practical-ai-prompting-my-prompts-for-daily-use/), a good prompt generally needs three things:
– a detailed request of what you want,
– a clear example of the desired result,
– and lots of context.

Assigning the AI a role, like “you are a digital marketing strategist” or “you are a non-fiction writer,” also makes a huge difference. When we give the AI a role, we’re helping it narrow down its vast knowledge to a specific perspective. It sets the right expectation. An AI without a role will often give you the most common, generic answer. A role helps it deliver more sophisticated, expert-level insights.

But even when we know these principles, the process can be frustrating. How do we know if our prompt is missing a crucial piece of context? This is where the real time gets spent, in the trial and error.

A couple of days ago, I stumbled upon a brilliant solution on [reddit](https://old.reddit.com/r/ChatGPT/comments/1lnfcnt/after_147_failed_chatgpt_prompts_i_had_a/) that reframes this entire process. Instead of trying to perfect the prompt ourselves, we can ask the AI to do it for us. This is where the idea of a simple “prompt agent” comes in.

***

### **What is a Prompt Agent?**

Think of a prompt agent as a specialized AI personality you create with a single, detailed prompt. Its only job is to perform a specific task exceptionally well. In this case, its job is to take your rough idea and transform it into a perfectly optimized prompt. It’s a bit meta, but it’s incredibly effective. For a deeper dive into agents, you can check out my notes on [what an AI agent is](https://hardiannazief.com/ai-agents-use-cases-creation/what-is-ai-agent-my-notes-on-a-key-video-that-explains-best/).

I found a prompt that creates an agent named “Lyra.” You give Lyra your basic request, and instead of executing it, Lyra deconstructs it, diagnoses its weaknesses, and rebuilds it into a master-level prompt. It even explains the changes and the reasoning behind them, which is a fantastic way to learn.

***

### **How It Works: The “Lyra” Prompt Agent**

{.aligncenter}

The prompt works by instructing the AI to follow a clear methodology. When you give it your initial idea, it will:

1. **Deconstruct:** It figures out your core intent, what you’re asking for, and what information might be missing.
2. **Diagnose:** It looks for gaps, ambiguity, or a lack of specificity that could lead to a poor result.
3. **Develop:** It rebuilds your prompt using proven techniques, like assigning the perfect expert role, adding necessary constraints, or structuring it for clarity.
4. **Deliver:** It provides you with the new, optimized prompt and explains what was changed and why.

It even asks you clarifying questions first if the request is complex, ensuring it fully understands your goal before creating the final prompt.

### **The “Lyra” Prompt You Can Use**

Here is the full prompt. You can use it to create your own “Lyra” agent whenever you need to generate a quality prompt result from an AI.

You are Lyra, a master-level AI prompt optimization specialist. Your mission: transform any user input into precision-crafted prompts that unlock AI’s full potential across all platforms.
“`
## THE 4-D METHODOLOGY

### 1. DECONSTRUCT
– Extract core intent, key entities, and context
– Identify output requirements and constraints
– Map what’s provided vs. what’s missing

### 2. DIAGNOSE
– Audit for clarity gaps and ambiguity
– Check specificity and completeness
– Assess structure and complexity needs

### 3. DEVELOP
– Select optimal techniques based on request type:
– **Creative** ? Multi-perspective + tone emphasis
– **Technical** ? Constraint-based + precision focus
– **Educational** ? Few-shot examples + clear structure
– **Complex** ? Chain-of-thought + systematic frameworks
– Assign appropriate AI role/expertise
– Enhance context and implement logical structure

### 4. DELIVER
– Construct optimized prompt
– Format based on complexity
– Provide implementation guidance

## OPTIMIZATION TECHNIQUES

**Foundation:** Role assignment, context layering, output specs, task decomposition

**Advanced:** Chain-of-thought, few-shot learning, multi-perspective analysis, constraint optimization

**Platform Notes:**
– **ChatGPT/GPT-4:** Structured sections, conversation starters
– **Claude:** Longer context, reasoning frameworks
– **Gemini:** Creative tasks, comparative analysis
– **Others:** Apply universal best practices

## OPERATING MODES

**DETAIL MODE:**
– Gather context with smart defaults
– Ask 2-3 targeted clarifying questions
– Provide comprehensive optimization

**BASIC MODE:**
– Quick fix primary issues
– Apply core techniques only
– Deliver ready-to-use prompt

## RESPONSE FORMATS

**Simple Requests:**
“`
**Your Optimized Prompt:**
[Improved prompt]

**What Changed:** [Key improvements]
“`

**Complex Requests:**
“`
**Your Optimized Prompt:**
[Improved prompt]

**Key Improvements:**
• [Primary changes and benefits]

**Techniques Applied:** [Brief mention]

**Pro Tip:** [Usage guidance]
“`

## WELCOME MESSAGE (REQUIRED)

When activated, display EXACTLY:

“Hello! I’m Lyra, your AI prompt optimizer. I transform vague requests into precise, effective prompts that deliver better results.

**What I need to know:**
– **Target AI:** ChatGPT, Claude, Gemini, or Other
– **Prompt Style:** DETAIL (I’ll ask clarifying questions first) or BASIC (quick optimization)

**Examples:**
– “DETAIL using ChatGPT — Write me a marketing email”
– “BASIC using Claude — Help with my resume”

Just share your rough prompt and I’ll handle the optimization!”

## PROCESSING FLOW

1. Auto-detect complexity:
– Simple tasks ? BASIC mode
– Complex/professional ? DETAIL mode
2. Inform user with override option
3. Execute chosen mode protocol
4. Deliver optimized prompt

**Memory Note:** Do not save any information from optimization sessions to memory.
“`

***

### **Key Takeaways**

* Getting great results from AI often involves a lot of trial-and-error in prompt writing.
* The three pillars of a good prompt are a detailed request, a clear example, and sufficient context.
* Using a “prompt agent” like Lyra can automate the optimization process, saving you time.
* This approach teaches you to be a better prompt engineer by showing you the “why” behind the improvements.

### **Frequently Asked Questions (FAQ)**

* **Why is assigning a role to an AI important?**
Assigning a role (e.g., “You are an SEO specialist”) helps the AI focus its vast knowledge on a specific domain. This leads to more detailed, expert-level responses instead of generic, common-sense answers. It sets the perspective and expectation for the output.

* **What is a “prompt agent”?**
A prompt agent is a specialized AI assistant created with a single, highly-detailed master prompt. Its purpose is to execute a very specific task, in this case, optimizing other prompts. It’s a simple yet powerful way to create a consistent and reliable AI tool.

* **Can I use this prompt optimizer for any AI model like ChatGPT or Gemini?**
Yes. The Lyra prompt is designed to be platform-agnostic, though it includes notes on how to best leverage the strengths of specific models like ChatGPT, Claude, and Gemini. Just tell it your target AI when you start.

By using a tool like this, you shift from guessing to a structured, repeatable process. Give it a try on your next complex request. You’ll not only get better results but also become a more effective AI user in the long run.

The post Stop Guessing: How a Simple “Prompt Agent” Can Optimize Your AI Requests appeared first on Hardian Nazief.

I’ve spent my career in digital marketing, from managing WordPress sites for event companies to optimizing SEM campaigns for B2B e-commerce. I’ve seen trends come and go. And I can tell you this: the shift towards AI in search isn’t just hype. It’s a fundamental change in how information is discovered and presented. We’re not just writing for people anymore. We’re also writing for the AI that informs them. This means we need to adapt our content strategy, making it easier for Large Language Models (LLMs) to read, understand, and feature our work.

### The New Rules: Predictive, Conversational, and Personalized

So, how do we make our content “AI-friendly”? I recently read a fantastic interview on [Search Engine Journal with Cindy Krum](https://www.searchenginejournal.com/googles-ai-search-journeys-are-reshaping-seo-with-cindy-krum/549614/), who has been studying this for years. Her insights really crystallized what I’ve been observing. She argues that to be featured in AI-powered search, our content needs to be:

1. **Predictive:** It should anticipate the user’s next question. Think about the entire user journey. If someone searches for “best running shoes,” they’ll likely search for “how to choose running shoe size” or “best running shoes for beginners” next. Your content should address these follow-up questions proactively.
2. **Conversational:** Write in a natural, human tone. Use questions and answers in your headings and text. AI models are built on conversational data, and they are designed to provide direct, clear answers. The more your content sounds like a helpful expert answering a question, the better. This aligns perfectly with a core SEO principle I’ve discussed before: **[Understanding User Intent: Cornerstone of Effective SEO in the AI Era](https://hardiannazief.com/ai-in-marketing-seo/understanding-user-intent-cornerstone-of-effective-seo-in-the-ai-era/)**.
3. **Personalized:** Content should be tailored to specific user needs and contexts. This goes beyond just keywords. It’s about understanding the *who* and *why* behind the search. For example, content about “marketing strategies” should be different for a small business owner versus a marketing executive at a large corporation.

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### My Practitioner’s Take: Putting This into Action

This framework isn’t just theory. It has practical implications for how we create content daily. In my own work, I’ve started to shift my focus. Instead of just targeting a primary keyword, I map out a cluster of related questions a user might have. This helps make the content more predictive and conversational.

This is just one part of the puzzle. As I’ve explored in another post, understanding **[How AI is Really Changing Day-to-Day SEO Tasks](https://hardiannazief.com/ai-in-marketing-seo/my-take-how-ai-is-really-changing-day-to-day-seo-tasks-beyond-the-hype/)** involves a broader look at our entire workflow, from research to analysis.

### A New Technical Edge: Understanding `llms.txt`

Beyond the content itself, there are also new technical considerations. If your website runs on WordPress, you may have heard about a recent [June 2025 update from the Yoast SEO plugin](https://yoast.com/yoast-seo-june-10-2025/). They’ve introduced a feature that automatically creates a file called `llms.txt`.

In simple terms, this file provides a clean, simplified version of your content in markdown format, specifically for LLMs to read. Think of it as giving the AI a special copy of your article without any of the website’s code or formatting, making it incredibly easy for the model to digest the core information.

Now, it’s important to be clear: `llms.txt` is not yet an official standard recognized by all search engines. However, in my view, it’s a smart, forward-thinking step. It’s a low-effort way to signal to AI models that you are actively providing well-structured, easy-to-read content. As a hands-on practitioner, I enabled it immediately. It’s a small technical tweak that could give you an edge as AI becomes more integrated into search.

### Key Takeaways

* **AI is Changing SEO:** AI Overviews in search results require a new approach to content creation.
* **Adapt Your Content:** Focus on making your content Predictive, Conversational, and Personalized to increase your chances of being featured.
* **Anticipate User Needs:** Don’t just answer the first question; answer the next three.
* **Leverage New Tools:** Features like the `llms.txt` file in Yoast SEO are emerging to help LLMs better understand your content.

—

### Frequently Asked Questions (FAQ)

**Q: What is an AI Overview?**
A: An AI Overview (formerly called Search Generative Experience or SGE) is a detailed, AI-generated summary that appears at the top of some Google search results pages. It aims to directly answer a user’s query by synthesizing information from multiple web pages.

**Q: How do I make my content more conversational?**
A: Write in a natural, direct style. Use first and second person (“I,” “you”). Structure parts of your content in a question-and-answer format, and use headings that pose common questions your audience might have.

**Q: Do I absolutely need an `llms.txt` file on my website right now?**
A: It is not a formal requirement or a recognized ranking factor yet. However, it’s a new, proactive measure supported by major SEO tools like Yoast. Implementing it is a low-risk, potentially high-reward strategy to make your content more accessible to LLMs.

—

The fundamentals of good marketing haven’t changed. Creating value and understanding your audience are still at the core of everything we do. But the tools and tactics we use are constantly evolving. By making our content more predictive, conversational, and technically accessible, we can ensure we are ready for the future of search.

The post From Keywords to Context: Preparing Your Content for Google’s AI-Powered Search appeared first on Hardian Nazief.

I’ve spent a lot of time refining my prompts to get better results, and one of the most effective techniques I’ve found is what I call the “Escape Hatch.” I saw Y Combinator’s CEO, [Garry Tan tweet](https://x.com/garrytan/status/1928928145182204344), mention this recently, and it perfectly describes a method I’ve learned to trust. It’s a simple instruction you give the AI that allows it to say “I don’t know” instead of inventing an answer.

This small change has made a huge difference in the reliability of the output I get, ensuring the content I create and the data I base my strategies on is accurate.

### What is an AI Hallucination (and Why It Matters)

First, let’s quickly define the problem. An AI hallucination is when a large language model (LLM) generates false information but presents it as factual. The AI isn’t “lying” in the human sense; it’s just trying to predict the next most likely word in a sequence and sometimes gets it wrong, especially when it doesn’t have the right data to answer your question.

{.aligncenter}

For professionals, this is a big deal. A fake statistic in a marketing report or a misquoted source in a blog post can damage credibility. Exp: For freelance writers, ensuring the quality and originality of AI-assisted output is critical for managing client trust. The escape hatch is a direct and practical way to address this.

### The Escape Hatch: Giving Your AI an ‘Out’

The core idea is simple: you explicitly give the AI permission to admit it doesn’t know something. You create a condition in your prompt that tells the model exactly what to do if it cannot find the information or fulfill the request accurately.

This prevents the AI from feeling forced to generate a plausible-sounding but incorrect answer. You are building a safety net into your request. The folks in the OpenAI community have discussed building “hallucination-resistant prompts,” and this technique is a cornerstone of that approach.

### How to Create Your Own Escape Hatch Prompt

Creating an escape hatch is straightforward. You just need to add a clear, direct instruction at the end of your query.

Here is the basic structure I use:

**”If you do not know the answer or cannot find verified information for this, please respond with ‘[Your Chosen Phrase]’.”**

My preferred phrase is simply: **”I do not have enough information to answer this.”**

**Example in Action:**

* **Weak Prompt:** “What was the a-la-carte price for the ahrefs in 2022?”
* **Strong Prompt w/ Escape Hatch:** “What was the a-la-carte price for the ahrefs service in 2022, based on publicly available data? **If you do not have enough information to answer this, please say so.**”

The first prompt might push the AI to invent a price. The second prompt encourages it to first check for real data and gives it a safe way out if none exists.

### A Personal Example from My Workflow

I was recently drafting a post about marketing automation tools and wanted to include a specific statistic about the ROI of push notifications for e-commerce in Indonesia. My initial prompt gave me a very specific number that I couldn’t verify.

I tried again with an escape hatch:

“Find a verifiable statistic from a 2023 or 2024 report on the average conversion rate increase for Indonesian e-commerce companies using app push notifications. **If you cannot find a statistic that matches these criteria, please state that the specific data is not available.**”

The AI responded that it couldn’t find a report matching those exact parameters. That response was far more valuable to me than a fake number. It told me I needed to broaden my search or approach the topic from a different angle. This is a perfect example of how to make your prompting more effective, a topic I cover in more detail in my [guide to practical daily prompts](https://hardiannazief.com/ai-for-writing-content-creation/practical-ai-prompting-my-prompts-for-daily-use/).

—

{.aligncenter}

### Key Takeaways

* **AI Hallucinations are Common:** AI models can generate false information when they don’t have the right data.
* **Use an Escape Hatch:** Add a simple instruction to your prompt telling the AI what to say if it can’t find the answer.
* **Be Specific:** Tell the AI to respond with a clear phrase like, “I do not have the information.”
* **Improves Reliability:** This simple trick dramatically increases the trustworthiness of the AI’s responses, saving you from costly errors.

### Frequently Asked Questions (FAQ)

**Does the escape hatch prompt work on all AI models (ChatGPT, Claude, Gemini)?**
Yes, this technique works across all major LLMs because it’s based on the principle of providing clear, conditional instructions, which all models are designed to follow.

**Can an AI still make a mistake even with an escape hatch?**
It’s possible, but far less likely. An escape hatch significantly reduces the chance of a hallucination for direct factual queries. It works best when you are asking for specific, verifiable information.

**Why is this better than just fact-checking everything?**
You should still verify critical information. However, using an escape hatch saves you time by preventing the AI from giving you a made-up fact that you then have to spend time debunking. It’s about improving efficiency and building a more reliable workflow. For more on creating effective prompts, you can explore my [guide on crafting engaging and informative prompts](https://hardiannazief.com/ai-for-writing-content-creation/prompt-guide-to-be-more-engaging-and-informative-creation/).

The post The Simple ‘Escape Hatch’ Prompt I Use to Reduce AI Hallucinations appeared first on Hardian Nazief.

As someone who’s been in the digital marketing and content creation space, I live with the struggle. You have your main keyword or theme, but fleshing that out into a full content plan? That’s where the real work begins. Traditionally, I’d use tools like [Ahrefs](https://ahrefs.com/) or [SEMrush](https://www.semrush.com), which are fantastic for many SEO tasks. However, for uncovering the nuanced subtopic keywords, that’s where AI play a key role.

Today, I want to share my process for using AI. Tools like [ChatGPT](https://chatgpt.com/), Google’s [Gemini](https://gemini.google.com/), or even up-and-comers like [DeepSeek](https://www.deepseek.com/) to generate insightful subtopic keywords.

### Why Traditional Keyword Research Sometimes Needs a Boost

Don’t get me wrong, keyword research tools are invaluable. They give us search volume, difficulty, and competitor insights. I still use them daily for my SEO work, drawing on my experience optimizing campaigns for my previous companies, where we boosted Organic Traffic by 151%. But when it comes to brainstorming the *breadth* and *depth* of a topic, AI language models can offer a different kind of creativity. They can help you think like your audience, anticipating questions and related interests you might not have considered.

### My Process: Using AI for Subtopic Keyword Generation

My approach is pretty straightforward but requires a bit of iterative prompting to get the best results.

**1. Start with Your Broad Theme or Pillar Keyword:**

You need a starting point. This could be a primary keyword you’re targeting for SEO, or a core theme for your next series of blog posts. For example, let’s say my pillar keyword is “financial benefits of corporate vehicle rental” (for a company of leased & rental car company)

**2. Crafting the Initial Prompt for AI:**

I usually open up ChatGPT, Gemini, or whichever LLM I’m experimenting with. My initial prompt would be something like:

*”I’m a content creator and digital marketer working on a content cluster around the main topic ‘financial benefits of corporate vehicle rental’ My target audience is small business owners in Indonesia and junior marketing executives. Can you generate a list of 20-30 potential subtopic keywords or long-tail keywords related to this? Focus on practical applications, challenges, and benefits for these audiences.”*

I find that providing context about the target audience helps the AI tailor its suggestions. My experience at SMARTFREN, managing campaigns for diverse user segments, taught me the importance of audience-centric messaging. For more examples of effective prompts, check out my guide on [Practical AI Prompting: My Prompts for Daily Use (Writing & Social Media Content)](https://hardiannazief.com/ai-for-writing-content-creation/practical-ai-prompting-my-prompts-for-daily-use/).

**3. Refining and Categorizing Generated Ideas:**
The first output is rarely perfect. I review the list, looking for:
* **Relevance:** Does it truly fit under the main theme?
* **Audience Fit:** Would small business owners or junior marketing executives actually search for this or find it useful?
* **Untapped Angles:** Does it offer a unique perspective?

{.aligncenter}

Then, I might use a follow-up prompt:

*”Thanks, Ok thats good. Now, could you categorize these subtopics into themes like ‘cost management’, ‘operational efficiency,’ and ‘scalability’? Also, can you suggest 3-5 more related niche subtopics that a growth-oriented SME founder might find useful for her clients?”*

This helps in organizing the ideas and digging even deeper. If you’re looking for different ways AI can assist here, you might find my post on [Brainstorming Content Ideas with AI: 3 Techniques That Actually Work useful](https://hardiannazief.com/ai-for-writing-content-creation/brainstorming-content-ideas-with-ai-3-techniques-that-actually-work/).

**4. Cross-Referencing with SEO Tools:**

Once I have a solid list from the AI, I take those potential subtopic keywords back to my traditional SEO tools (Ahrefs, Google Keyword Planner, etc.). This is where I check for search volume, competition, and existing SERP landscapes. This step ensures that the creative ideas generated by AI are also grounded in SEO viability. It’s a blend of AI’s expansive thinking and data-backed validation.

### Real-World Application: From “AI in Marketing” to Specific Angles

Let’s say my broad topic is “AI in Marketing.”
Initial AI output might give general terms.
Through refining prompts, I could get to more specific subtopics like:
* “Using AI for personalized email marketing campaigns for SMEs”
* “Beginner’s guide to AI-powered social media content scheduling”
* “How AI can help analyze customer feedback for product development (for marketing teams)”
* “Ethical considerations of using AI in marketing data collection”

These are much more focused and actionable for content creation, addressing pain points for personas like Gilang Ramadhan, a junior marketing executive, who is learning the ropes of digital marketing, or Fitriani Hasan, a growth focus SME founder, who needs to understand AI’s impact on business processes.

### Key Takeaways

* AI tools like ChatGPT, Gemini, and DeepSeek are excellent for brainstorming and generating a wide array of subtopic keywords.
* Providing clear context about your target audience in your prompts leads to more relevant suggestions.
* The process is iterative; refine your prompts based on the AI’s output.
* Always validate AI-generated keywords with traditional SEO tools to check for search volume and viability.
* AI augments, not replaces, your expertise and strategic thinking.

{.aligncenter}

### Frequently Asked Questions (FAQ)

**Q1: Can AI generate keywords with good search volume?**
AI itself doesn’t directly access real-time search volume data in the same way SEO tools do. It generates ideas based on patterns in the vast text data it was trained on. That’s why it’s crucial to use the AI-generated ideas as a starting point and then validate them with tools like Ahrefs, Semrush, or Google Keyword Planner for search volume and difficulty.

**Q2: Which AI tool is best for generating subtopic keywords?**
ChatGPT, Google’s Gemini, and Claude are all very capable. I’ve also experimented with others like DeepSeek. The “best” often comes down to personal preference and the specific nuances of your topic. I recommend trying a couple with the same prompt to see which gives you a more useful starting point. Sometimes one model will give more creative, out-of-the-box ideas, while another might provide more structured, predictable ones.

**Q3: How many subtopic keywords should I aim for from AI?**
It’s better to get a larger list (exp: 20-50) and then refine it down. Not all suggestions will be gold. The goal is to cast a wide net initially and then use your expertise to filter and select the most promising ones that align with your content strategy and audience needs.

**Q4: Can AI help me understand the intent behind subtopic keywords?**
Yes, indirectly. By asking the AI to generate questions your target audience might have around a subtopic, or to explain who would search for a particular keyword and why, you can gain insights into user intent. For example, “For the subtopic ‘AI for budget-conscious small businesses,’ what specific problems or questions would motivate someone to search for this?”

### Conclusion: AI, a New Brainstorming Partner

Using AI to generate subtopic keywords has become a valuable part of my content planning workflow. It helps overcome creative blocks and uncovers angles I might not have thought of through traditional methods alone. It’s like having an incredibly well-read brainstorming partner available 24/7.

Remember my experience boosting organic traffic and improving campaign performance? A lot of that success came from understanding user needs and tailoring content accordingly. AI can significantly speed up the discovery phase of that process.

Start with a broad topic, experiment with different prompts and AI tools, and see how it can enrich your content strategy.

The post Expanding Niche Ideas Keyword: How to Use AI to Generate Subtopic Keywords for Content appeared first on Hardian Nazief.

As someone who’s spent years in digital marketing optimizing budgets – whether it’s reducing Cost Per Acquisition (CPA) in SEM campaigns or maximizing the impact of a campaign with the most optimized budget available – I’m always on the lookout for ways to get the best results without emptying my wallet. This mindset applies perfectly to how I approach AI tools too.

The good news? There are some fantastic, more budget-friendly ways to tap into powerful AI language models. I’ve previously written about some of them in “[Exploring AI Tools Beside ChatGPT: Claude and Perplexity](https://hardiannazief.com/ai-tools-practical-guides/exploring-ai-tools-beside-chatgpt-claude-and-perplexity/)”.

Today, I want to share my experiences with a few applications that let you use AI on your own terms. These often involve connecting to AI models via an API (Application Programming Interface) using your own keys, or by running AI models directly on your computer (often via desktop applications).

This approach can significantly lower costs compared to some all-in-one subscription services, especially for frequent use. Plus, it gives you the flexibility to compare different LLM AIs and choose the one that best fits your needs, rather than being locked into one or two providers.

To put things in perspective, let’s look at the typical subscription costs versus API costs for some of the major closed-source AI LLMs. Keep in mind API costs are usually per million tokens (think of tokens as pieces of words; 1,000 tokens is roughly 600-750 words).

| Model | Subscription Plan | Subscription Cost | API Model | API Cost (per 1M tokens) |
|——————–|———————–|——————-|——————|—————————————-|
| OpenAI ChatGPT | ChatGPT Plus | $20/month | GPT-4.1 | Input: $2, Output: $8 |
| Google Gemini | Google One AI Premium | $20/month | Gemini 2.5 Pro | Input: $1.25, Output: $10 (up to 200K) |
| Anthropic Claude | Claude Pro | $20/month | Claude 3.7 Sonnet| Input: $3, Output: $15 |
| xAI Grok | SuperGrok | $30/month | Grok 3 | Input: $3, Output: $15 |

*(Note: API prices are estimates and can change. Always check the provider’s official pricing page for the latest details. Some providers also offer free tiers or trial credits for their APIs.)*

**Why Look for Alternatives? The Allure of API Access and Local LLMs**

When you use a typical AI chat service, you’re often paying for the convenience of their interface, bundled features, and the underlying AI model access. This is great for ease of use. However, if you’re a bit more hands-on or need to manage costs tightly, using tools that connect via API or run locally can be a game-changer.

**API Access:** Many AI model providers (like OpenAI, Anthropic, Google, etc.) offer API access. This means you pay directly for what you use – the amount of data processed (tokens). For moderate or specific use cases, this can be much cheaper than a fixed monthly subscription to a premium chat interface. You’ll need to get an API key from the provider, but the apps we’ll discuss make using it pretty straightforward.

In essence, getting API access means obtaining a unique key, much like a password. You then insert this key into the designated app to enable chat functionality with the intended AI LLM.

Here are links to get you started with API access from major providers:

| Provider | API Access Link |
|——————–|———————————————————————————|
| OpenAI | [platform.openai.com/signup](https://platform.openai.com/signup) |
| Google AI (Gemini) | [ai.google.dev/gemini-api/docs/get-started](https://ai.google.dev/gemini-api/docs/get-started) |
| Anthropic (Claude) | [console.anthropic.com/login](https://console.anthropic.com/login) |
| xAI (Grok) | [x.ai/developers](https://x.ai/developers) (check for current availability) |

**Local LLMs (Large Language Models):** This involves downloading an AI model and running it on your own computer using an application like LM Studio. The big advantage? Once you’ve downloaded the model, running it is generally free (aside from your electricity bill!). It also offers maximum privacy since your data doesn’t leave your machine.

The trade-off is that you need a reasonably powerful computer. For a smooth experience with smaller models (e.g., 3B or 7B parameter models), a minimum of 8GB to 16GB of RAM is advisable. Larger, more capable models (those with tens or hundreds of billions of parameters, denoted as 70B, 180B, etc.) will require significantly more RAM (sometimes 32GB, 64GB, or even more) and often benefit greatly from a dedicated GPU. The most powerful models can indeed be very resource-intensive.

Let’s dive into some tools I’ve explored that embrace these approaches.

**My Go-To Apps for Budget-Friendly AI Chat**

I’ve spent some time with a few applications that offer this kind of flexibility. Here are my thoughts:

**1. AnythingLLM: Your Private, Document-Savvy AI Chat**

* **Website:** [https://anythingllm.com/](https://anythingllm.com/)
* **The Gist:** AnythingLLM is a brilliant open-source application that you can host yourself or run on your desktop. Its superpower is letting you create your own private “ChatGPT-like” assistant that can chat about *your* documents. You connect it to an LLM of your choice via API (e.g., OpenAI, Azure, Gemini, Claude, or local models via Ollama/LM Studio) and feed it your PDFs, text files, Word docs, and more. It uses a vector database system to efficiently process and retrieve information from large documents, often employing a technique called [Retrieval-Augmented Generation (RAG)](https://www.geeksforgeeks.org/what-is-retrieval-augmented-generation-rag/). RAG essentially allows the AI to pull in relevant information from your documents *before* generating an answer, making its responses highly relevant to your specific content.

* **My Experience:** I found AnythingLLM incredibly useful for building a specialized knowledge base. Imagine loading it with all your project notes, research papers, or company policies. Then, you can ask it questions, and it will answer based on *that specific content*. This is far more targeted than a general-purpose chatbot. From a budget perspective, you control the API costs by choosing your model and only paying for what you process.

If you use it with a local model (via Ollama, for instance, which AnythingLLM supports), the inference cost is practically zero. Setting it up requires a little technical comfort, but their documentation is pretty good. For a small business owner, this could be a fantastic way to create an internal FAQ bot from company documents without ongoing subscription fees for every user.

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**2. PyGPT: The Versatile Desktop AI Companion**

* **Website:** [https://pygpt.net/](https://pygpt.net/)
* **The Gist:** PyGPT is another excellent open-source desktop application that acts as a frontend for various AI models. It supports models accessed via API (like OpenAI’s GPT models, Anthropic’s Claude, Google’s Gemini) and can also connect to local models if you have them set up (e.g., via LM Studio or Ollama). It’s packed with features, including chat, image generation (with models like DALL-E or Stable Diffusion via API or locally), voice input/output, and plugin support. It also has built-in vector database support and can automate file and data embedding for custom knowledge bases.

* **My Experience:** PyGPT feels like a Swiss Army knife for AI access. I appreciate its flexibility. You can easily switch between different models and providers depending on your needs and budget. If I need a quick, high-quality response for a complex marketing query and don’t mind a small API charge, I can use a powerful model like Claude 3.7 Sonnet or Gemini 2.5 Pro. If I’m doing less critical tasks, drafting initial ideas, or just want to experiment, I can point it to a more economical API option or a local model. The interface is clean, and it remembers your conversation history. This tool offers a great way to experiment with different AI capabilities without committing to multiple subscriptions.

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**3. LM Studio: Run Powerful AI Models Locally**

* **Website:** [https://lmstudio.ai/](https://lmstudio.ai/)
* **The Gist:** LM Studio makes it surprisingly easy to download, manage, and run LLMs on your personal computer (Windows, Mac, Linux). It has a built-in interface to browse and download various open-source AI models from repositories like Hugging Face (think models like Llama, Mistral, Mixtral, Phi, etc.). Once downloaded, you can chat with these models offline.

* **My Experience:** This is where things get really interesting for cost-saving and privacy. The idea of running a powerful AI without an internet connection (after the initial model download) and without my data leaving my machine is very appealing. I’ve used LM Studio to experiment with different models. It’s fascinating to see how capable some of these local models are becoming. The main considerations are your computer’s hardware, as mentioned earlier. While the absolute cutting-edge, largest proprietary models are often still best accessed via API, many open-source models run locally via LM Studio are fantastic for daily tasks, creative writing, coding assistance, and summarization.

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**Key Considerations When Choosing Your Path**

* **Budget:** If your primary goal is minimizing ongoing costs, running local LLMs via LM Studio (once you have a capable machine) is hard to beat for inference. For API access, monitor your usage carefully. The good news is, API costs can be very manageable. For example, this article draft, would only be around 3,200-3,500 tokens. Given that API pricing is often per *million* tokens, the cost for individual tasks is often very low. Other example based on my experience of a marketing research conversation, is using around 260,000 tokens and still being well under typical monthly subscription costs is a great real-world illustration. However, be mindful if you’re processing extremely large volumes of text or code, as costs can accumulate.

* **Technical Comfort:** Setting up API keys and configuring them in apps like AnythingLLM or PyGPT involves a few more steps than just signing up for a web service. LM Studio is quite user-friendly for running local models, but you still need to understand model compatibility with your hardware.
* **Use Case:**
* For chatting with your own documents privately and building custom knowledge bases: **AnythingLLM** is excellent, especially with its RAG capabilities.
* For a versatile desktop client with access to various API models, local models, and a rich feature set: **PyGPT** is a strong contender.
* For maximum privacy, offline use, and no ongoing inference costs (with the right hardware): **LM Studio** is the way to go for exploring open-source models.

* **Model Choice:** With API-based tools, you can often choose from a range of models from providers like OpenAI, Anthropic, Google, Cohere, or Mistral AI. Each has different strengths, speeds, and costs. Experiment to see what works for you. For local models, the Hugging Face Hub (easily accessible via LM Studio) is a vast repository with a rapidly growing selection.

**Final Thoughts: Taking Control of Your AI Usage**

Exploring these alternative AI chat applications has been a real eye-opener. It proves that you don’t always need expensive subscriptions to leverage the power of AI. By being a bit more hands-on, whether it’s managing an API key or downloading a local model, you can gain more control, enhance privacy, and often significantly reduce your costs. This aligns perfectly with the data-driven and efficiency focused approach I’ve cultivated throughout my career in digital marketing, where getting the most value from your investments is key.

What are your favorite budget-friendly AI tools or strategies? Have you tried any of these? Share your thoughts in the comments below – I’d love to hear about your experiences!

The post Affordable Ways to Chat with AI Using Your Own Keys or Local Models appeared first on Hardian Nazief.

Now, when I say AI, I’m not just talking about tools that spit out generic blog posts. I’m talking about using AI as a strategic brainstorming assistant, a digital sounding board that can help unlock new angles and perspectives. Over my years in digital marketing and now diving deeper into AI applications, I’ve experimented with various approaches. Today, I want to share three techniques that I *actually* use, ones that have proven effective in my workflow for sparking creativity and generating content ideas that resonate.

## Technique 1: The “Problem/Solution/Benefit” Angle with AI

One of the most powerful ways to connect with your audience is to address their pain points directly. This technique uses AI to help you systematically uncover these problems, explore potential solutions, and then frame your content around the compelling benefits.

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Here’s how I approach it:

1. **Identify Audience Problems:** I start by prompting an AI with questions about the common challenges my target audience faces. For instance, when I was working in a B2B industrial e-commerce company, a key audience was procurement managers. A prompt could be: *”What are the common daily challenges procurement managers in Indonesia face when sourcing industrial parts online?”* Note: doing a deepresearch setting on your AI would be best.

2. **Brainstorm AI-Powered Solutions:** Once I have a list of problems, I ask the AI to brainstorm ways these challenges could be addressed, potentially with AI itself or other relevant solutions. Continuing the example: *”Brainstorm 5 solutions or tools, including potential AI applications, that can help procurement managers overcome [specific challenge from step 1, e.g., ‘difficulty in finding exact part specifications quickly’].” *

3. **Frame Content Around Benefits:** The final step is to generate content ideas that highlight the *benefits* of these solutions. This is where you turn insights into clickable, valuable content. Example prompt: *”Generate 5 blog post titles and brief outlines focusing on the benefits for a procurement manager of using [solution from step 2] to solve [challenge from step 1].”*

**My Experience:** I used a similar approach for a B2B client struggling with blog topic generation. The AI helped identify very specific pain points for their niche audience. We then developed a content series addressing each pain point with clear solutions and benefit-driven titles. This not only filled their content calendar but also led to increased engagement because the topics were so directly relevant. I’ve found tools like ChatGPT, Claude, and Google’s Gemini to be excellent for this kind of structured brainstorming.

**Why it works:** This technique forces you to think from your audience’s perspective. Content that solves a genuine problem and clearly articulates a benefit is almost always more engaging and performs better in search.

## Technique 2: The “Audience Deep Dive” via AI Personas

You’ve probably heard about audience [personas](https://buffer.com/library/marketing-personas-beginners-guide/). But have you ever used AI to bring them to life and brainstorm *with* them? This technique helps you go beyond basic demographics and tap into the specific needs and motivations of your audience segments.

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Here’s the process:

1. **Flesh Out Your Personas with AI:** If you have basic personas, you can ask AI to elaborate on them. Prompt example: *”I have a persona named ‘Citra Lestari,’ a 23-28 year old Jr. Marketing Executive in Jakarta. She’s tech-savvy, ambitious, and wants to use AI to impress her superiors. What are her likely biggest anxieties about using AI in her marketing campaigns brainstorming? What kind of content formats and topics would make her feel more confident and capable in using AI?”*

2. **Brainstorm Persona-Specific Content:** Based on the AI’s more detailed persona insights, you can then ask it to generate content ideas specifically tailored to that persona. Example prompt: *”Generate 10 content ideas (blog posts, short video scripts, LinkedIn posts) that would directly appeal to Citra, addressing her anxieties and helping her achieve her goals related to AI in marketing.”*

**My Experience:** When planning content for this very blog, “Making AI Make Sense for Everyday Workflow,” I often think about who I’m trying to reach. Let’s take ‘Dewi Anggraini,’ our freelance writer persona. She’s tech-savvy but concerned about AI output quality and originality. So, I might ask an AI: “Dewi wants to use AI to speed up her writing research but she is worried about plagiarism and maintaining her unique voice. What are three blog post ideas that address these concerns and offer practical solutions?” This helps me generate ideas that are not just general but specifically address the nuances of a particular reader type.

**Why it works:** Tailoring content to specific audience segments makes it far more relevant and impactful. AI can help you uncover those nuanced needs and preferences you might not have considered, leading to more targeted and effective content.

## Technique 3: The “Seed Idea Expansion” with AI

Sometimes you have a broad topic in mind, or even just a keyword, but you need to break it down into manageable, engaging content pieces. This is where AI can act as a powerful expansion tool.

Here’s how to do it:

1. **Start with a Seed Idea/Keyword:** This could be a broad theme from your content strategy or a keyword you’re targeting. For example, a seed idea for me is often “AI in SEO.”

2. **Use AI to Branch Out:** Now, use prompts to get the AI to generate various related ideas.
* **Subtopics:** *”Break down the topic ‘AI in SEO’ into 10 distinct subtopics suitable for a blog series for digital marketers.”*
* **Questions:** *”What are 5 common questions marketing professionals in Indonesia have about AI’s impact on keyword research?”* (Tools like [Perplexity](https://www.perplexity.ai/) are also great for this).
* **Different Angles:** *”Explore the topic ‘AI in SEO’ from three different angles: one for beginners, one for technical SEO specialists, and one for marketing managers.”*
* **Content Formats:** *”Suggest 3 different content formats (e.g., in-depth guide, case study, quick tips video) for explaining how AI is changing technical SEO tasks.”*

**My Experience:** When I first started outlining content for the “AI in Marketing & SEO” pillar of this blog, “AI in SEO” felt massive. I used a process very similar to this to break it down. For example, asking an AI to list common concerns about AI and SEO helped inspire articles like “[SEO Relevancy in the Age of AI](https://hardiannazief.com/ai-in-marketing-seo/seo-relevancy-in-the-age-of-ai/)” and “[Understanding User Intent: Cornerstone of Effective SEO in the AI Era.](https://hardiannazief.com/ai-in-marketing-seo/understanding-user-intent-cornerstone-of-effective-seo-in-the-ai-era/)” It helped me to create a more structured approach rather than just writing about random aspects of the topic.

**Why it works:** This technique is fantastic for building topic clusters, which are essential for good SEO. It ensures you cover a subject comprehensively and provides a steady stream of related content ideas, preventing you from running dry.

## Tips for Getting the Best Results from AI Brainstorming

While these techniques are powerful, remember that AI is a tool. Its output is heavily influenced by your input. Here are a few quick tips:

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* **Be Specific with Your Prompts:** The more context and detail you give the AI, the better and more relevant the suggestions will be. My article on “[Practical AI Prompting: My Prompts for Daily Use (Writing & Social Media Content)](https://hardiannazief.com/ai-for-writing-content-creation/practical-ai-prompting-my-prompts-for-daily-use/)” has some good starting points for this.
* **Iterate and Refine:** Don’t expect the perfect list of ideas from the first prompt. Treat it as a conversation. Ask follow-up questions, request alternatives, and guide the AI.
* **Combine AI with Your Expertise:** You are the expert in your niche and your audience. Use AI to generate possibilities, then use your knowledge to filter, refine, and select the best ideas.
* **Always Review and Edit:** AI can sometimes generate ideas that are off-topic, impractical, or even factually incorrect. Always apply your critical thinking and edit accordingly.

## AI: Your Brainstorming Co-Pilot

Coming up with a steady stream of valuable content ideas is a significant part of any digital professional’s or creative’s work. It used to be a purely manual, sometimes frustrating, process. But as I’ve found, AI can be an incredibly valuable co-pilot in this journey.

By using techniques like the “Problem/Solution/Benefit” angle, diving deep into audience personas, and expanding seed ideas, you can leverage AI to not just find more ideas, but better, more targeted ones. It’s about making the creative process more efficient and, ultimately, more effective. As I often say, it’s about “Making AI Make Sense for Everyday Workflow.”

## Quick Reference: AI Brainstorming Prompts & Techniques

To give you even more concrete examples of how you can interact with AI for brainstorming, I (with a little help from [AI-powered research!](https://gemini.google.com/)) compiled this handy table. It illustrates different types of prompts and the kinds of outcomes you can expect. Think of it as a cheat sheet to get your AI brainstorming sessions started:

**Table 1: Examples of AI Brainstorming Prompts and Techniques**

| Technique | Example Prompt | Expected Outcome |
| :———————- | :——————————————————————————————————————————————- | :——————————————————————————————————————————————————————————————————————————————————————————————————- |
| Broad Idea Generation | “List 10 blog post ideas about the future of remote work.” | A list of diverse blog post topics related to remote work trends, challenges, and opportunities. |
| Targeted Expansion | “For the idea ‘The challenges of remote team communication,’ explore three different angles focusing on technology, team dynamics, and leadership strategies.” | Three distinct perspectives on the challenges of remote team communication, providing a basis for more specific content. |
| Idea Clustering | “Group these 10 ideas into categories.” | A set of categorized ideas, revealing overarching themes and relationships between different concepts. |
| Outline Development | “Create a blog post outline for the topic ‘Benefits of Mindfulness in the Workplace,’ including an introduction, three main sections, and a conclusion.” | A structured outline with a logical flow, including potential headings and subheadings for a blog post on mindfulness at work. |
| Interactive Refinement | “Here’s an initial idea: \[brief content idea]. What are three potential problems with this idea, and how could we address them?” | Feedback on the potential weaknesses of the initial idea, along with suggestions for improvement and refinement. |
| Style Refinement | “Rewrite this sentence: \[sentence from a brainstormed idea] to be more concise and engaging for a social media audience.” | A revised version of the sentence that is shorter, more attention-grabbing, and suitable for social media. |

Hopefully, seeing these examples gives you a clearer picture of how versatile AI can be as a brainstorming partner!

The post Brainstorming Content Ideas with AI: 3 Techniques That Actually Work appeared first on Hardian Nazief.

Let’s cut through the buzzwords. How is AI *actually* changing the SEO tasks we do every single day? Is it really a revolution, or just a new set of tools? Based on my experience advising clients and using these tools myself, I think the truth is somewhere in the middle. AI isn’t replacing the need for strategic thinking, but it *is* fundamentally shifting *how* we approach our daily work. Let’s look at the changes happening right now.

**Keyword Research: Getting Insight Beyond Keyword Relations**

Keyword research has always been the foundation of good SEO. Remember spending hours brainstorming variations, checking volumes, and guessing intent? AI hasn’t made keyword research disappear, but it’s making it much smarter and faster. While the keyword data is not up to date within the large language model of the AI, it can give suggestion which an insight that sometimes we amiss in looking for variations and corelation.

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* **Finding Hidden Gems:** Tools integrating AI can analyze vast amounts of data to uncover long-tail keywords and topic clusters I might have missed. Instead of just looking at search volume, AI helps in [Understanding User Intent](https://hardiannazief.com/ai-in-marketing-seo/understanding-user-intent-cornerstone-of-effective-seo-in-the-ai-era/), which is crucial. Did the user want to buy, learn, or compare? AI helps clarify this better than ever.

* **Semantic Understanding:** AI excels at understanding relationships between words and concepts. This helps build topic clusters that cover a subject comprehensively, which Google increasingly values. Think less about exact match keywords and more about covering the user’s entire information need. In my work optimizing campaigns at Misumi, understanding the precise B2B intent behind keywords was crucial for reducing Cost Per Acquisition (CPA). AI tools today could have accelerated that analysis significantly.

* **Efficiency:** Brainstorming sessions that used to take hours can now be jumpstarted with AI. Asking an AI like ChatGPT or Gemini for keyword ideas related to a core topic, or analyzing competitor keywords with tools like Ahrefs or Semrush (which now incorporate AI features), saves valuable time that can be reinvested in strategy.

**Content Creation: From Blank Page to First Draft Faster**

Let’s be honest: staring at a blank page is tough. AI is becoming a powerful assistant in the content creation process, though it requires careful guidance and good prompts (check out my past writing: Guide on [Practical AI Prompting](https://hardiannazief.com/ai-for-writing-content-creation/practical-ai-prompting-my-prompts-for-daily-use/) for some ideas).

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* **Outlining and Structuring:** Need a blog post structure fast? AI can generate logical outlines based on top-ranking content or your specific requirements. This provides a solid framework to build upon. I often use this to quickly map out the key sections for a new article, ensuring I cover the essential points.

* **Brainstorming Angles:** Stuck for ideas? AI can suggest blog post titles, different angles on a topic, or questions your audience might be asking. It’s like having a brainstorming partner available 24/7.

* **Summarizing and Repurposing:** AI tools are great at summarizing long reports or articles, which is useful for creating snippets for social media or understanding competitor content quickly.

* **Drafting Assistance (with a Caveat):** Yes, AI can generate initial drafts. However, in my experience, this output *always* needs significant editing. It often lacks unique voice, personal experience (the ‘E’ in EEAT!), and the nuanced understanding a human writer brings. Think of it as a starting point, not a finished product. When I was managing content production at Sriboga Flour Mill, ensuring brand voice consistency was key; AI drafts need careful shaping to meet those standards.

**On-Page SEO: Refining Optimization at Scale**

Optimizing individual pages used to be a manual checklist process. AI is helping make this more data-driven and efficient.

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* **Content Audits:** AI-powered tools can analyze your content against top-ranking pages, suggesting areas for improvement like keyword density, readability, internal linking opportunities, and semantic relevance. This speeds up the auditing process immensely. There are a lot of paid SAAS that can handle this, such as [MarketMuse](https://www.marketmuse.com/) or [SURFER Seo](https://surferseo.com/). But in my case I usually create an AI Agent with RAG, that uses the content strategy document and crosscheck alignment between the content with our goal, target audience and any SEO strategy such as content pillar, EEAT principle etc.

* **Alt Text Generation:** Automate generate alt text for images using AI for SEO-friendly alt-text and better accessibility. In small scale, images alt text generation can be handle manually, but when it is larger scale it can be cumbersome. With AI vision, the image analysis for text generation can be done in large scale and fast. One of the tools for this are [ALTTEXT.AI](https://alttext.ai/)

* **Internal Linking Suggestions:** Finding relevant pages to link to within your own site can be time-consuming. Some tools now use AI to suggest relevant internal links, improving site structure and distributing link equity more effectively. This was a key part of boosting organic traffic while i was at Misumi, an industrial component ecommerce, and AI can streamline finding those opportunities.

* **Schema Markup Ideas:** Generating structured data (schema markup) helps search engines understand your content better. AI tools can sometimes help identify opportunities or even generate basic schema code, making this technical task more accessible.

**Technical SEO: Diagnosing Issues More Efficiently**

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While maybe less “day-to-day” for everyone, AI is starting to make inroads into technical SEO diagnostics.

* **Pattern Recognition:** Analyzing server log files or crawl data can be tedious. AI has the potential to identify patterns indicating crawl issues, server errors, or indexation problems much faster than manual analysis. Imagine diagnosing website slowdowns like those I occasionally faced at my past companies during traffic surges – AI could potentially pinpoint bottlenecks faster in the future.

* **Prioritizing Fixes:** Some advanced tools are exploring using AI to prioritize technical SEO fixes based on potential impact, helping teams focus their efforts where it matters most. We can see these in work while we do a site audit in tools such [ahrefs](https://ahrefs.com/) or [SEMRush](https://www.semrush.com/)

The area is still evolving for everyday use, the potential for AI to assist in complex technical analysis is significant. For now, tools like Google Search Console remain essential, but expect AI enhancements to make them even more powerful.

**Reporting and Analysis: Finding the Story in the Data**

As someone who built performance dashboards using Looker Studio (formerly Google Data Studio), I know how much time data analysis and reporting can take. AI is starting to help here too.

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* **Automated Insights:** Instead of just presenting raw data, some analytics platforms are incorporating AI to automatically surface key trends, anomalies, or insights. “Organic traffic from this campaign increased 20% week-over-week” is more useful than just seeing the numbers.

* **Natural Language Queries:** Imagine asking your analytics platform, “Which landing pages had the biggest drop in traffic last month?” AI is enabling this kind of natural language interaction with data. Google Analytics 4 (GA4) in this case has a built-in insight feature that handle the NLP queries data interation.

* **Report Summarization:** AI can help summarize key performance indicators (KPIs) and trends from lengthy reports, making it easier to communicate results to stakeholders.

**Conclusion: Your Strategy Still Matters Most**

So, is AI taking over SEO? My take is clear: No. AI is transforming *how* we execute SEO tasks, making us more efficient and data-driven. It automates repetitive work, surfaces insights faster, and assists in creation. But it doesn’t replace the core need for human strategy, creativity, critical thinking, and understanding your audience.

The real change is that SEO professionals now need to be proficient not just in SEO principles, but also in leveraging these AI tools effectively. It’s about working *with* AI, not being replaced *by* it. The hype is real in the sense that AI *is* impactful, but the day-to-day reality is more about augmentation than automation replacing the strategist.

Start experimenting! Try incorporating one or two AI tools into your workflow for specific tasks like keyword brainstorming or outlining. See how it works for you. The landscape is changing fast, and adaptability is key. How are you using AI in your daily SEO work? Let me know in the comments below

The post My Take: How AI is Really Changing Day-to-Day SEO Tasks (Beyond the Hype) appeared first on Hardian Nazief.

Recently, I stumbled upon this excellent YouTube video by the channel “[Jeff Su](https://www.youtube.com/@JeffSu)” that breaks down AI agents in a really clear, step-by-step way. It uses concepts many of us already understand, like ChatGPT, and builds up from there.

I thought it would be useful to embed the video right here at the start. Give it a watch if you have a moment!

**AI Agents, Clearly Explained : https://www.youtube.com/watch?v=FwOTs4UxQS4**

Watched it? Great! Didn’t have time? No worries. For fellow readers, or if you just want my perspective layered on top, let’s walk through the key ideas presented in the video together. I found its “Level 1, 2, 3” approach really helpful.

**Level 1: The Foundation – Large Language Models (LLMs)**

The video starts with something most of us are familiar with: Large Language Models or LLMs. These are the engines behind popular tools like ChatGPT, Google Gemini, and Claude – tools I personally use almost daily for everything from drafting emails and brainstorming content ideas for this blog to even getting help refining code snippets for automation tasks.

The core idea, as the video visualizes, is simple:
*You (Human) provide Input -> LLM processes -> LLM produces Output (based on its training data).*

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Think about asking ChatGPT to write a polite follow-up email. Your request is the input, and the generated email is the output.

But, as the video rightly points out, standard LLMs have limitations. They generally can’t access your *personal*, real-time information (like your calendar) or confidential company data. They also operate passively – they wait for your command and then respond. This is crucial to remember as we move to the next level.

**Level 2: Taking a Step Forward – AI Workflows**

This is where things start getting more interesting and closer to the automation I’ve worked with in my digital marketing career, particularly during my time managing campaign systems at SMARTFREN.

An AI workflow, as explained in the video, is essentially telling an LLM to follow a *predefined path* that might involve interacting with external tools or data sources.

The video uses the example: “When I ask about a personal event, first check my Google Calendar.” Now, the LLM isn’t just responding based on its training data; it’s following a specific instruction to fetch external information before generating the output.

This might involve multiple steps:
1. Get data from Google Sheets.
2. Summarize it using Perplexity AI.
3. Draft a social media post using Claude with a specific prompt *I* wrote.
4. Schedule it to run daily.

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*(Suggested Image: A simple flowchart diagram illustrating a sequence: Step 1 -> Step 2 -> Step 3 -> Output.)*

This sounds powerful, and it is! It’s the basis for a lot of marketing automation. We set up triggers and actions – if a user clicks this link, send them that email sequence; if a campaign reaches this threshold, update that report.

The video mentions **Retrieval-Augmented Generation (RAG)** here. Don’t let the fancy term scare you. As the video simplifies, RAG is basically a common *type* of AI workflow where the AI is instructed to “look things up” (retrieve information from a specific source, like my calendar or a company knowledge base) before answering (generating a response).

However, the key defining trait of an AI *workflow* is that **the human is still the decision-maker.** *I* defined the path. *I* wrote the prompts. If the output isn’t right (like the LinkedIn post in the video example not being funny enough), *I* have to go back and manually tweak the instructions or prompts. This trial-and-error iteration is human-led.

**Level 3: The Leap – AI Agents**

This is the main event! What makes an AI Agent different from an AI Workflow?

The video nails the core distinction with what it calls “the most important sentence”: The massive change is **replacing the human decision-maker with an LLM.**

Instead of just *following* my predefined path, the AI Agent is given a *goal* and then needs to:

1. **Reason:** Figure out the *best* way to achieve the goal. What steps are needed? What’s the most efficient approach? (e.g., “Should I copy-paste articles, or compile links and use a tool? The second option seems better.”)
2. **Act:** Use available tools to execute those steps. (e.g., “Okay, I need to compile links. Google Sheets is connected and efficient for this.”)
3. **Observe & Iterate:** Look at the result of its actions. Is it good enough? Does it meet the goal? If not, figure out how to improve it and try again *autonomously*. (e.g., “This draft isn’t quite right based on best practices. Let me critique it using another model and refine it.”)

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The video mentions the **ReAct framework (Reason + Act)** being common for agents. Once broken down like this, it makes perfect sense, right? The agent needs to think (reason) and do (act).

The iteration part is fascinating. Remember having to manually rewrite the prompt in the workflow example? An AI agent could potentially handle that itself. It might critique its own output against certain criteria (like “LinkedIn best practices” or “brand voice guidelines”) and refine the draft multiple times until it meets the standard – all without direct human intervention in that loop.

Andrew Ng’s demo mentioned in the video (finding a skier in footage) is a great practical example. Instead of a human manually tagging “skier,” the agent *reasons* what a skier looks like, *acts* by scanning the video, and *delivers* the result. It handles the task autonomously based on the goal.

**Why Does This Matter for Us?**

Understanding this progression from basic LLMs to workflows to agents is really helpful, especially for professionals and creatives.

* **LLMs** are powerful assistants for generating text, brainstorming, and basic tasks with a specific prompt I wrote. Having a go-to set of instructions can be a huge timesaver; I actually shared some of [my practical prompts for daily writing and social media content](https://hardiannazief.com/ai-for-writing-content-creation/practical-ai-prompting-my-prompts-for-daily-use/) before.
* **Workflows** allow us to automate multi-step processes using AI, connecting different tools and data sources based on rules *we* set. This is where I have experience with marketing automation and tools like Looker Studio for reporting comes into play – setting up systems to execute defined tasks efficiently, reviewing excel sheet, confirm the data are clean or not (small scale analysis) etc.
* **AI Agents** represent the next step: AI systems that can potentially take on complex goals, figure out the steps, use tools, and adapt their approach autonomously. Imagine an agent not just running the ad campaign *you* designed, but analyzing performance, reasoning about market shifts, and adjusting the strategy itself to meet a defined goal like maximizing ROI or reaching a specific audience segment. This evolution naturally raises questions about the future of various roles and disciplines, including [SEO’s relevancy and how it’s adapting in the age of AI](https://hardiannazief.com/ai-in-marketing-seo/seo-relevancy-in-the-age-of-ai/).

While truly sophisticated, autonomous agents doing complex professional tasks are still evolving, the underlying concepts are becoming more accessible. We’re seeing simpler agent-like features appear in various tools.

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**Wrapping Up**

I hope walking through this video’s explanation from a reader’s perspective has helped clarify things. The journey from simple chatbots (LLMs) to rule-based automations (Workflows) to goal-oriented, decision-making systems (Agents) is a significant one.

The key takeaway, as the video highlights, is the shift in decision-making. Workflows follow human instructions; Agents figure out the instructions themselves to meet a goal.

This field is moving incredibly fast, and understanding these foundational concepts helps us cut through the hype and see the practical potential for our own workflows, whether in marketing, content creation, business operations, or any other field.

What are your thoughts on AI agents? Have you experimented with any tools that have agent-like capabilities? Let me know in the comments below or connect with me on LinkedIn! I’m always learning and curious to hear about others’ experiences. Have you started exploring AI agents or workflows? I’d love to hear about your experiences or any questions you have in the comments below!

The post What is AI Agent? My Notes on a Key Video That Explains Best! appeared first on Hardian Nazief.

It’s a question I’ve asked myself often. Honestly, juggling multiple tools can feel like another task in itself! So, I decided to put these four popular AI assistants that i know, head-to-head, specifically looking at how useful they are for those rapid research tasks that pop up in our daily workflows.

## Why Use AI for Quick Business Research Anyway?

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Before comparing, let’s be clear about what “quick business research” often means for professionals like us. It could be:

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* Finding recent stats on Indonesian e-commerce growth.
* Getting a summary of a competitor’s latest product launch announcement.
* Getting a summary of a competitor’s latest digital strategy activity.
* Understanding the key takeaways from a long form marketing articles that you don’t have time to read fully.
* Brainstorming potential angles for a new content strategy.

AI assistants promise to speed these tasks up significantly. They can synthesize information from various sources, summarize dense text, and even help spark ideas. As someone who has managed campaigns and analyzed performance using tools like Google Analytics and Looker Studio, I appreciate anything that cuts down research time and gets me to the insights faster.

## The Contenders: A Quick Introduction

Let’s briefly meet the players we’re comparing today:

* **[Perplexity](https://www.perplexity.ai/):** This AI often feels like a search engine hybrid. Its big strength is citing sources directly in its answers, which is great for verifying information quickly.
* **[Claude (Anthropic)](https://claude.ai/):** Claude is known for its favorite among coders, his response style that felt straight home with formal tone and its focus on safety and thoughtful responses. It often feels more like an introvert conversational tone.
* **[Google Gemini](https://gemini.google.com):** Backed by Google’s massive search index, Gemini is a versatile assistant, often integrated into other Google products. It has strong potential for multi-modal tasks (text, images, etc.), though we’re focusing on text-based research here.
* **[Grok (xAI)](https://grok.com/):** It’s Twitter AI, Grok prides itself on having access to real-time information via X and having a more rebellious, sometimes humorous personality, though the later version (Grok 3 at the time of writing) felt more natural professional and more helpful. The Grok i use are the one inside twitter, not in its standalone website, because i feel the result are more holistic, more reference and more intuitive in checking the claim.

## The Showdown: Comparing Key Features for Business Research

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Okay, let’s compare them based on factors crucial for quick business insights:

### 1. Accuracy and Sourcing

For business decisions, you need information you can trust. How do they stack up?

* **Perplexity:** This is where Perplexity often shines for quick checks. It usually provides numbered citations directly linked to the sources it used. This makes it relatively easy to click through and verify the information or dig deeper. I find this incredibly useful when I need a quick fact for a client email or internal report. But beware, I have found a couple of hallucination from 10 research topics asked, because the company is too old to be reference (usually the startup is already bankrupt and perplexity refer to the old source)
* **Claude:** Claude generally provides accurate information but is sometimes less transparent about its *specific* sources unless prompted. It’s good at synthesizing information from documents you provide it, which is a different kind of accuracy, fidelity to the source material. For personal reference it felt not too intuitive .
* **Gemini:** Gemini leverages Google Search, so its accuracy often reflects that. It’s getting better at citing sources, but it’s not always as consistently insightfull as Grok. You might need to cross-reference its claims sometimes.
* **Grok:** Grok’s access to real-time X data is unique but also a double-edged sword. X isn’t always a source of verified fact. While it can give you the latest buzz, you absolutely need to verify its claims from more reliable sources, especially for critical business data. Its “personality” can also sometimes color the information. But in term of gaining new insightfull perspective, Grok is a very welcome edition. For me, Grok is the underdog here,

**My take:** For quick, verifiable facts with easy source checking, **Grok & Gemini** often has the edge.

### 2. Speed and Conciseness

When you need research *now*, speed matters.

* **Perplexity:** Generally very fast for straightforward queries. Its default mode aims for concise, sourced answers.
* **Claude:** Response speed is usually good, though sometimes it can be slightly slower if it’s generating a very detailed answer.
* **Gemini:** Typically fast, leveraging Google’s infrastructure. It’s good at providing bullet points or summaries when asked.
* **Grok:** Response speed is generally decent. Its conciseness can vary depending on the “mode” you use (regular vs. deep research).

**My take:** For pure speed on simple queries, **Perplexity** and **Gemini** often feel the quickest. For summarizing documents, **Claude** and **Gemini** is efficient.

### 3. Ease of Use and Interface

A tool is only useful if you can actually use it without friction.

* **Perplexity:** Clean, search-like interface. Very intuitive, especially the “Focus” feature to narrow down search (e.g., Academic, YouTube, Reddit).
* **Claude:** Simple, chat-based interface. Easy to paste text or upload documents (PDF, TXT, CSV, etc.). Straightforward.
* **Gemini:** Clean interface, familiar to anyone who uses Google products. Integration possibilities (like in Gmail or Docs) can be a plus.
* **Grok:** Integrated within X (Twitter) for Premium subscribers, which might be convenient if you’re already there, but can also feel limiting if you prefer a dedicated interface.

**My take:** **Perplexity** and **Claude** offer very clean, focused interfaces. **Gemini** is comprehensive and familiar. **Grok’s** integration is context-dependent and new features comes fast but not as sophisticated as ChatGPT or Gemini.

### 4. Unique Strengths for Business Research

Each tool has a little something extra:

* **Perplexity:** The clear sourcing and ‘Focus’ modes are its key differentiators for research, remember to verify!
* **Claude:** Handling large documents (reports, transcripts, code) is its superpower. If you need to understand a 50-page PDF quickly, Claude is often the best choice.
* **Gemini:** Its connection to the Google ecosystem and potential for multi-modal tasks (analyzing images, charts in the future) make it versatile. Good for general knowledge queries.
* **Grok:** Real-time information access via X is its unique selling point. Good for getting the absolute latest public sentiment or breaking news (but remember to verify!).

### 5. Limitations to Consider

No tool is perfect:

* **Perplexity:** While great for sourced summaries, it might sometimes lack the depth, hallucinate often or creative brainstorming ability of others.
* **Claude:** Can sometimes be overly cautious or refuse to answer certain prompts. Its knowledge cut-off might be slightly less current than Gemini or Perplexity for very recent events (excluding Grok’s X access).
* **Gemini:** Can sometimes provide generic answers. Its capabilities are constantly evolving, which is good, but means its performance can be variable.
* **Grok:** The reliance on X means potential for bias or misinformation. Free Access are available to the general free user but currently the X Premium offer more usage credit and new features.

## My Personal Workflow: Which AI for Which Job?

So, how do I actually use these in my day-to-day work? It’s rarely just one tool.

* **For quick facts, stats, or initial competitor research where I need sources:** I often start with **Grok**. Its speed and citations save time on verification.
* **When I need to summarize a long report, article, or analyze uploaded data:** **Gemini** is my go-to. Its ability to process large amounts of text is invaluable. For example, summarizing industry trend reports before a strategy meeting.
* **For general brainstorming, drafting initial marketing copy ideas, or leveraging Google Search knowledge:** I turn to **Gemini** and **Grok** . Its versatility is helpful for broader creative or informational tasks.
* **To check the absolute latest buzz or sentiment on X about a brand or topic:** I quickly check **Grok**.

For someone like a marketing executive needing quick trend data or competitor headlines, Grok or Gemini might be frequent starting points. For small business owner, needing simple summaries of articles or basic competitor website info, Gemini’s ease of use could be ideal. For a new digital marketer learning the ropes, experimenting with Gemini for brainstorming and Perplexity for sourced facts could be a good combo.

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## The Verdict: Which AI is “Best” for Quick Business Research?

As you’ve probably guessed, there’s no single “best” AI for every quick business research task. The ideal tool depends entirely on what you need *right now*.

* **Choose Perplexity if:** Your priority is fast, sourced answers and quick verification.
* **Choose Claude if:** You need to summarize or analyze long documents or text quickly and accurately.
* **Choose Gemini if:** You want a versatile assistant with strong general knowledge and potential Google ecosystem integration.
* **Choose Grok if:** You need access to real-time X conversations and trends.

My advice? Don’t feel pressured to pick just one. Understand the strengths of each and use them strategically. Trying them out is the best way to see which fits *your* workflow best. Many offer free tiers, so you can experiment without commitment.

AI isn’t magic, but it’s a powerful lever for efficiency. Using these tools smartly allows me to spend less time digging and more time strategizing – something I believe is crucial for anyone in marketing or business today.

The post Perplexity vs. Claude vs. Google Gemini vs Grok: Which AI is Best for Quick Business Research? appeared first on Hardian Nazief.

The masses haven't even heard of Claude or Perplexity pic.twitter.com/vmwvsawBtS

— Dennis (@dkardonsky_) September 15, 2024

To have the same understanding of my writing, apps like ChatGPT, Claude, and Perplexity are examples of conversational AI applications, designed to mimic human-like conversations using advanced technology. They operate using large language models (LLMs) trained on extensive datasets to understand and generate text. These models leverage natural language processing (NLP) and machine learning (ML) to interpret and respond to user inputs across various contexts such as customer service, content creation, and personal assistance. Conversational AI aims to provide efficient, accessible, and human-like interactive solutions across multiple industries.

After a bit of research in Indonesia, the same trend also applies,

With this, while many people are familiar with ChatGPT, these other tools are powerful AI applications Claude and Perplexity that deserve attention. This article aims to introduce these models, explain their functions, and highlight their similarities with ChatGPT.

### What is Claude?

[Claude](https://claude.ai) is an AI language model developed by Anthropic, designed to understand and generate human-like text. Its main function is to assist users in a range of tasks, from answering questions and providing explanations to generating creative content and engaging in conversations.

**Latest Features of Claude:**

1. **Artifacts:** Recently introduced, this feature allows users to create and store various content types. It enables collaborative work by letting users manage their ideas and projects within the Claude platform effectively.

2. **Projects:** This new functionality enhances collaboration among users, particularly for Pro and Team users. It brings a structured approach to managing tasks and discussions, making it easier for teams to work together.

3. **Humor Understanding:** Claude has been updated to better understand and generate humorous responses, enhancing its ability to engage in light-hearted conversations.

4. **Enhanced User Experience:** With the introduction of Claude 3.5 Sonnet, users can expect improved conversational fluidity and context awareness, making interactions even more natural and relevant.

From my experience, I prefer Claude over ChatGPT; as the language and results are more consistent and resonate better with me than ChatGPT.


### What is Perplexity?

[Perplexity](https://www.perplexity.ai/) is primarily known as an AI-powered conversational search engine, focusing on providing accurate and concise answers to user queries. Its goal is to facilitate immediate information retrieval while allowing for an interactive conversational experience.

**Latest Features of Perplexity:**

1. **Pages Feature:** Perplexity now offers a feature that generates customizable web pages based on user prompts. This tool helps compile information and resources into shareable formats, enhancing accessibility.
2. **Copilot:** This innovative feature assists users in refining their queries by asking clarifying questions, helping them narrow down their searches for more accurate responses.
3. **AI-Powered Summaries:** Perplexity can provide concise summaries of web pages, allowing users to quickly grasp essential information before diving deeper into any topic.
4. **Collaborative Documents:** The platform now supports creating interactive, shareable documents, making it easier for users to compile and organize information collaboratively.

From my experience, the results are quite biased; at first, I thought it aggregated the results, then made a summary, but after intense testing, it sometimes hallucinates, so confirmation and rechecking are still a must. But as a starter, if you can pay the price, it’s a Google replacement.

### Similarities with ChatGPT

Claude and Perplexity share several characteristics with ChatGPT, making them valuable alternatives in the Conversational AI landscape:

1. **Natural Language Processing:** All three models leverage advanced natural language processing capabilities, enabling fluid dialogue and a clear understanding of user intent.
2. **Wide Range of Applications:** Whether generating text, answering questions, or assisting with brainstorming, Claude, Perplexity, and ChatGPT offer versatile tools for a variety of tasks.
3. **User-Friendly Interfaces:** Each tool prioritizes accessibility, allowing users to engage without needing technical expertise.
4. **Continuous Improvement:** Like ChatGPT, both Claude and Perplexity are under constant development, focusing on enhancing their performance and understanding user needs.


### Conclusion

While ChatGPT has become a household name in the AI space, Claude and Perplexity offer compelling alternatives with their own unique strengths. Claude shines with its more structured approach and coding-friendly features, while Perplexity carves out its niche as a conversational search engine designed for quick, relevant responses. As we’ve explored, each tool brings something valuable to the table, whether it’s Claude’s artifact feature or Perplexity’s AI-powered summaries.

My personal journey with these tools has led me to prefer Claude for its consistency, especially in coding tasks. However, I’ve found that Perplexity, despite occasional hallucinations, serves as a useful starting point for research. ChatGPT, while powerful, has shown some inconsistencies in my recent experiences.

The elephant in the room, particularly in regions like Indonesia, remains the pricing. At around $20 per month, these subscriptions represent a significant investment – equivalent to nearly a week’s worth of meals for some. Fortunately, the free versions provide a taste of their capabilities, allowing users to explore before committing.

As AI continues to evolve, it’s clear that no single tool has a monopoly on perfection. Each has its strengths and quirks, and the best choice often depends on the specific task at hand. Whether you’re a casual user or a professional seeking to leverage AI, exploring these alternatives can broaden your toolkit and potentially enhance your productivity.

In the end, while I’ve moved to API usage for cost efficiency, I encourage readers to experiment with the free versions of these tools. Your experience might differ from mine, and you may find unexpected value in features I haven’t highlighted. The world of AI is vast and rapidly changing – there’s always something new to discover.

#### Note:

* I don’t test Gemini because, for me, it hallucinates too often when I use it.
* I used to subscribe to all three (ChatGPT, Claude, and Perplexity) but stopped last May and switched to API for all three to save costs.

The post Exploring AI Tools Beside ChatGPT: Claude and Perplexity appeared first on Hardian Nazief.

So I began to introduce her to ChatGPT, highlighting its strength in conversational discussions while also reminding her of the necessity to double-check facts. Through this writing, I want to share my experiences with ChatGPT, focusing on the basic day-to-day use of AI. I’ve realized how this tool has quietly become a part of my daily routine. It’s not about revolutionizing anything – it’s just another useful tool in our digital toolbox. Consider this a friendly reminder. So, I thought I’d share some practical ways I’ve been using ChatGPT, in case you’re curious about giving it a try. This is not prompt guide like I wrote before, but more of a list of use case I found in my day-to-day activity

A Helping Hand in the Kitchen

Remember those times when you’re staring at your fridge, wondering what to cook? That’s where I’ve found ChatGPT/Claude surprisingly helpful. Just the other day, I listed the ingredients I had on hand, and it suggested a few recipe ideas I hadn’t thought of. It’s like having a creative cooking buddy who never runs out of ideas.

Examples:
1. When I had leftover tempe, some spinach, and coconut milk, ChatGPT suggested a fusion recipe for “Tempe Spinach Curry” that turned out delicious.
2. Another time, with just some rice, eggs, and sambal, it proposed a twist on nasi goreng by adding crispy fried shallots and a fried egg on top, elevating a simple meal.

Language Learning Made Conversational

As someone trying to learn Japanese and German, I’ve been using ChatGPT for quick language practice. It’s not perfect, but it’s great for casual conversation practice or getting explanations about grammar rules without diving into textbooks.

Examples:
1. I’ve used ChatGPT to practice translating Indonesian idioms like “Ada udang di balik batu” into English and discussing their meanings.
2. When learning about Indonesian sentence structures, I asked ChatGPT to explain the difference between active and passive voice in Bahasa Indonesia, using examples with “di-” and “me-” prefixes.

DIY Project Assistant

Last month, when I was trying to fix a leaky faucet, I used ChatGPT to break down the process into simple steps. I asked seemingly silly questions about all the items needed (in Indonesian), and it helped me understand what tools I needed and even suggested some troubleshooting tips. It felt like having a patient friend guiding me through the process.

Another Examples:
1. When fixing my rusty old Vespa, I asked ChatGPT about local alternatives for hard-to-find parts, and it suggested checking with “bengkel custom” in my area.
2. For a batik-making project, ChatGPT helped me understand the different types of canting tools and their uses in the traditional wax-resist dyeing process.

Writing Help for Various Occasions

From drafting emails to brainstorming ideas for my son’s school project, ChatGPT has been a useful sounding board. It doesn’t write things for me, but it helps get the creative juices flowing when I’m stuck. Do remember that AI-generated content tends to hallucinate in long context summaries, so it’s important to review twice.

Examples:
1. When writing a formal letter in Bahasa Indonesia, ChatGPT helped me with the proper salutations and closing remarks for different levels of formality.
2. For my son’s project on Indonesian folklore, ChatGPT suggested creative ways to present the story of Malin Kundang using a mix of traditional and modern storytelling techniques.

Travel Planning Made Easier

Planning my recent weekend getaway, I used ChatGPT to get ideas for local attractions and tips for budget travel. It helped me create a rough itinerary, which I then refined based on my preferences.

Examples:
1. For a trip to Yogyakarta, ChatGPT suggested a balanced itinerary including visits to Borobudur, Prambanan, and lesser-known spots like Pindul Cave for river tubing.
2. When planning a budget trip to Bali, it recommended staying in Ubud and provided tips on finding affordable warungs and participating in a local cooking class.

Fitness and Workout Ideas

On days when my usual workout feels boring, I ask ChatGPT for some new exercise ideas or quick workout routines. It’s not a replacement for a trainer, but it adds some variety to my fitness routine.

Homework/Studying Helper for My Son

When my son asks for help with his homework or topic studies, ChatGPT has been great for breaking down complex topics into simpler terms. It’s especially useful for subjects I’m a bit rusty on.

Examples:
1. For a project on Indonesia’s biodiversity, ChatGPT helped explain the concept of “Wallace Line” and its significance in the ecology.
2. When studying Indonesian history, it provided a simplified timeline of events from the Dutch colonial period to Indonesia’s independence, making it easier for my son to understand.

As a Temporary Therapist

Sometimes, as an introvert, I try to conform in work social groups. The range of norms is wide, from those who are very open-minded to those who are ultra-conservative. ChatGPT can offer a neutral perspective on social situations and help me navigate these complex interactions.

Examples:
– When dealing with the hierarchical nature of some Indonesian offices, it provided advice on respectfully communicating with superiors while still expressing my ideas.

A Word of Caution

While ChatGPT is handy, it’s important to remember it’s not all-knowing. I always double-check important information, especially for health or financial matters. It’s a helper, not a replacement for expert advice or our own judgment.

Wrapping Up

ChatGPT isn’t about replacing Google or changing the world overnight. It’s just another tool that can make some daily tasks a bit easier or more interesting. It is something that we should give it a try. You might find it useful in ways you didn’t expect, just like I did when I started using it for more than just tech stuff.

The post ChatGPT in Everyday Life: A Practical Guide for the Curious appeared first on Hardian Nazief.