In the first article, we explored why rankings alone no longer explain visibility in AI-powered search. This time, we take a closer look at candidate page selection—a concept we use at Lumar to help explain why some pages become eligible for retrieval while others don’t.

We’ll explore why candidate page selection matters for GEO, how it differs from traditional rankings, and what influences whether a page becomes eligible for retrieval.

Short on time? Here are the key takeaways…

Candidate selection isn’t the same as ranking

Traditional SEO is built around rankings. Once a page is indexed, the goal is to improve its position in search results. Rankings fluctuate over time, but every eligible page is competing for visibility.

Candidate page selection is different. Instead of determining where a page appears, it determines whether that page is considered as a potential source for a particular query.

From what we’ve seen, this stage has traditionally been largely invisible because standard SEO tools don’t measure it. Rankings, keyword positions and traffic show how a page performs in search results, but they can’t tell you whether it was ever considered for an AI-generated response.

That’s why strong rankings don’t always translate into AI visibility. A page may rank highly in traditional search yet never contribute to an AI-generated response if it isn’t selected as a candidate.

Why candidate selection exists

If candidate selection determines which pages are eligible for retrieval, the next question is why AI systems need this extra stage at all.

To generate a coherent response, AI systems need an efficient way to identify useful source material. Evaluating every indexed page or every possible passage for every query wouldn’t be practical, so candidate selection narrows the search space to a smaller pool of trustworthy pages.

That also explains why page-level signals matter so much. Before individual passages can contribute to a response, the page itself needs to establish that it’s relevant to the query and appropriate to retrieve from.

What influences candidate page selection?

Candidate page selection appears to be influenced by a combination of lexical, semantic, authority and freshness signals. Their relative importance changes depending on the query. A breaking news search, for example, is likely to place greater emphasis on freshness, while a medical query may depend more heavily on authority and trust.

Lexical signals

Despite advances in semantic search, traditional keyword signals still play an important role. Exact and near-exact matches in titles, H1s, headings and anchor text provide strong topical cues, helping search systems identify what a page is primarily about.

Semantic relevance

AI systems also appear to evaluate whether a page is genuinely about the topic being searched for, rather than simply containing matching keywords. Pages that explore a subject in depth are more likely to become candidates than those that only mention it in passing.

At this stage, the focus is on establishing broad topical relevance before identifying the strongest individual answer.

Learn more about semantic relevance for GEO/AEO

Authority and trust

Authority also appears to be important during candidate selection, particularly for YMYL (Your Money or Your Life) topics where inaccurate information could have real-world consequences, such as medical, financial or legal advice.

In our experience, official documentation, government guidance, recognized organizations and established publishers are more likely to be considered for these types of queries. For broader informational searches, authority is balanced alongside topical relevance and content quality.

Freshness

Freshness doesn’t influence candidate page selection equally for every query. Evergreen content may remain relevant for years, while news, product updates and other rapidly changing topics often favor more recent sources.

Candidate selection depends on the query

Whether a page becomes a candidate depends on the query being asked. A page may be considered a strong source for one search, but never enter the candidate pool for another.

For example, a general automotive blog might be a suitable source for a query such as “how oil changes work.” A much more specific search, such as “Honda Civic oil drain torque,” is more likely to favor manufacturer documentation or specialist technical resources, where accuracy and precision carry greater weight.

Why traditional SEO tools don’t measure candidate selection

Traditional SEO tools are built around the assumption that indexed pages are eligible to compete for visibility. From there, they measure performance through rankings, keyword positions, backlinks and traffic.

Candidate selection introduces an earlier stage that sits outside those metrics. A page may be fully indexed and perform well in traditional search, yet never become a candidate for a particular AI-generated response.

As a result, existing SEO tools can’t tell you whether a page entered the candidate pool, why it may have been excluded, or which signals influenced that decision. Understanding AI visibility will require ways of measuring performance beyond rankings alone.

What this means for GEO

Candidate selection starts at the page level, so optimization should too. A page with a clear, well-defined topic is easier for AI systems to interpret than one trying to cover multiple unrelated subjects. Aligning titles, H1s and headings with the queries people actually use to search helps reinforce that topical focus.

Once the page’s intent is clear, credibility becomes just as important. For queries where trust carries greater weight, the quality and authority of the source may influence whether the page is considered for retrieval at all.

Only then does it make sense to optimize individual passages. If the page never becomes a candidate, even the strongest content is unlikely to contribute to an AI-generated response.

What comes next

Candidate selection determines which pages are eligible for retrieval. The next stage is deciding which parts of those pages are actually used to generate a response.

In the next article, we’ll look at: 

• How AI systems evaluate semantic relevance once a page becomes a candidate
• Why the same content can be interpreted differently at different stages of retrieval, and 
• How chunk-level retrieval ultimately determines which passages contribute to an AI-generated response.

The post Candidate Page Selection: The Stage SEO Tools Don’t Measure appeared first on Lumar.

This is the first article in our Generative Engine Optimization (GEO) series, where we explore how AI-powered search is changing SEO—and how SEO teams can successfully adapt. 

We’ll first look at why rankings are no longer the best way to understand search visibility. Then, the rest of the series takes a closer look at the concepts shaping AI search, from semantic relevance to authority. 

Only got 30 seconds? Here are our key takeaways:

For the past 20 years, search optimization has revolved around one question:

“Where do I rank?”

This made sense when search engines returned lists of blue links and users chose which result to click. It makes far less sense when the interface generates a single answer instead.

Generative AI hasn’t killed search, but it has changed how information is selected and presented. Yet many SEO teams still rely heavily on traditional ranking metrics, even though rankings alone no longer explain visibility.

This article explores why the traditional SEO mental model no longer fits AI-powered search, and what that means for search visibility.

Why rankings alone no longer guarantee visibility

Traditional search engines were designed to present users with a ranked list of results. In most cases, the interface looked like this: 

• Multiple results were returned
• Results were ordered by relative relevance
• Users decided which result to click

SEO’s focus on rankings emerged because that’s how users navigated search. Generative search changes the interface—and with it, the way visibility is earned.

Rather than presenting users with a page of ranked links, AI can now:

• Collapse the Search Engine Result Page (SERP) into a single answer 
• Synthesize information from multiple sources 
• Prioritize relevance and trustworthiness

Content also needs to be recognized as a source that can inform the generated response.

Generative engines don’t just “rank” — they cite

To generate a response, AI systems first identify sources they can use.

For GEO, that means creating content that AI systems recognize as relevant, trustworthy and useful for the query being answered.

In practice, that means producing content that is:

• Precise, answering the query clearly and directly.
• Semantically rich, with enough semantic recall to cover the topic comprehensively.
• Unique, offering information or perspectives that add value beyond competing content.
• Designed to answer real user questions, rather than simply targeting keywords.
• Fresh, particularly where information changes over time.

Content quality, however, is only part of the picture. Brand presence, authority signals and clear content structure also influence how AI systems interpret and use information.

Read Tech GEO: Technical Website Fixes to Optimize for AI Inclusion

The GEO retrieval pipeline (simplified)

In our experience, it can be helpful to think about AI-powered search as a series of stages that determine whether content is ultimately used in a generated response. 

A simplified version looks like this:

1. Availability and indexability
   Can the content be discovered, accessed, and processed?
2. Candidate page selection
   Which pages appear relevant to the topic and credible enough to be considered as potential source material?
3. Chunk retrieval
   Which sections of those pages best address the user’s question?
4. Answer assembly and attribution
   Which passages provide clear, reliable information that can support the generated response?

Step 2 is where most content quietly fails. It isn’t a ranking; it’s closer to a yes/no. A page that never enters the candidate pool can contain the perfect answer and still be invisible, because no passage inside it is ever retrieved. That’s the stage traditional analytics can’t show you.

Why page context still matters

AI-generated responses often reference specific passages rather than entire pages. Those passages, however, don’t exist in isolation.

Google has recently clarified that “chunking” content into smaller pages isn’t a GEO strategy. Its systems can understand long-form content and identify the most relevant sections without requiring pages to be artificially split up.

The broader context of the page helps AI systems interpret individual passages. A well-written paragraph is far more useful when it’s supported by comprehensive, topically relevant content.

This is why GEO extends beyond optimizing individual paragraphs. The surrounding context helps establish the topical focus and credibility that AI systems use to interpret individual passages.

Different queries prioritize different signals

The type of question being asked influences which sources are most useful to an AI-generated response. Queries where accuracy is critical aren’t evaluated in quite the same way as those that are open-ended or exploratory.

Let’s look at two examples:

Example 1: “Honda Civic oil change”

Someone looking for instructions on maintaining their car expects accurate, reliable guidance. 

For safety-critical, YMYL-adjacent queries like this, AI-generated responses are generally more likely to reference sources that demonstrate strong expertise and trust, such as vehicle manufacturers, recognized repair manuals and established automotive publications.

This doesn’t mean smaller publishers can’t be cited, but demonstrating credibility becomes especially important when accuracy has real-world consequences.

Example 2: “Best productivity hacks for students”

This is a non-YMYL, exploratory query with no single definitive answer. A wider range of sources may be useful, provided they address the topic clearly and comprehensively.

In this case, topical relevance and the quality of the content may carry more weight than institutional authority alone.

The relative importance of authority, topical relevance and content quality depends on the information the user is looking for.

Semantic relevance is doing different jobs

Semantic relevance is often discussed as though it’s a single signal, but it contributes to several aspects of how content is understood.

At the page level, it establishes the overall topic and scope of the content. Within individual passages, it helps connect specific questions with the information that answers them.

Strong semantic recall comes from covering a topic in sufficient depth and making relationships between concepts clear. Pages with broader topical coverage provide more context for both readers and AI systems.

What’s next

This is the first article in our GEO series. The rest of this series explores that framework in more detail, including:

• Candidate selection and how AI systems identify potential source material
• Semantic relevance across different stages of retrieval
• Chunkability and its role in AI citations
• Authority and trust signals
• Measuring GEO performance beyond traditional SEO metrics

The post From SEO to GEO: Why Rankings Alone Won’t Get You Visibility in AI Search appeared first on Lumar.

Each month, Lumar’s in-house tech SEO experts hand-pick some of the SEO industry’s top news stories from across the web to keep you up-to-date on all things SEO and website optimization.

For our July 2026 SEO & GEO/AEO news roundup, the top headlines include:  

Dive into the details behind the SEO and GEO/AEO headlines this month below…

Google’s 2026 spam update completes its rollout in just two days

It took Google just two days to finish rolling out its second spam update of  the year. . Unlike  March’s spam update, this one didn’t include  any noteworthy changes to its spam policies, nor were there any announcements about specific behaviour being penalised. For sites affected, Google advises reviewing its spam policy documentation in detail. Site owners should also be prepared that recovery from spam-related penalties typically takes months rather than days.

(Source: Google Search Status Dashboard)

Data shows continued decline in CTR for 2026

New data from Advanced Web Ranking indicates that CTR for top organic positions continues to decline year over year. , particularly on SERPs  featuring AI Overviews and other rich features. The data highlights a widening  gap between ‘clean’ SERPs with 10 organic results and feature-heavy results pages, where AI summaries, ads and other rich results compete for attention. Rankings  still matter, but visibility is increasingly  decoupled from clicks as zero-click behaviour rises.

(Source: Advanced Web Ranking)

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Google clarifies how impressions work in AI Overviews and AI Mode

After some initial confusion among SEOs, John Mueller has confirmed that only links — not brand mentions — count as impressions in Google’s AI search features. Links must also be visible to the user to be counted. If a user needs to expand or interact with an AI feature before a link is displayed, the impression is only recorded once that link becomes visible..

(Source: John Mueller via BlueSky)

Google doubles-down on its ‘Good SEO is Good GEO’ messaging

A new Google Think article was published last month by Brendon Kraham, VP of Search & Commerce Global Ads Solutions at Google. Kraham explains how AI-led features like AI Overviews and AI Mode are designed to surface helpful, high-quality content from the existing search index. He reiterates that success in this environment still depends on strong technical SEO, useful content and a focus on user needs, rather than AI-specific optimisation tactics.

The article also positions AI Search as an extension of traditional search systems, with existing ranking and quality signals still central to visibility in generative search.

(Source: Think With Google)

Study finds just 3% of llms.txt files were accessed by AI systems in May

A new study from Ahrefs analysed 137,000 domains that were live in May 2026. 28% of those had published an llms.txt file yet 97% of those files received zero requests..

Of the small subset that did get traffic, AI-related crawlers accounted for a minority share. Most traffic instead came from SEO tools, research bots and general crawlers. The study further raises questions about the practical value of llms.txt files for AI visibility,  particularly if maintaining one comes as the expense of  higher-priority tasks.

(Source: Ahrefs)

Microsoft Clarity adds robots.txt violation tracking to Bot Analytics

Microsoft has added a new feature in Clarity’s Bot Analytics dashboard that  flags when bots request URLs blocked by a site’s robots.txt file. The update allows site owners to monitor violation rates, track trends over time and break down activity by bot, operator, and content type.

The feature also highlights which sections of a site are being accessed despite restrictions, offering a clearer view of how AI crawlers and automated systems interact with published content and crawl directives.

(Source: Microsoft)

Google downplays the benefits of markdown for AI understanding… and then surfaces markdown in AI overviews

In the latest Search Off the Record episode, Martin Splitt and John Mueller discuss whether websites should convert content to Markdown to improve AI visibility. These tactics have been gaining a lot of traction recently, especially via the launch of features like Cloudflare’s “Markdown for AI Agents”. Google’s argument  is that modern crawlers and LLMs are already well-equipped to process standard HTML, including complex page structures. Therefore, they  don’t require simplified markdown formats.

 That advice, however,  appeared to be contradicted when one user spotted actual Markdown appearing in Google’s AI Overview snippets. At the very least, this   confirms that Google’s own AI systems are accessing Markdown and seemingly using it as part of the response generation process.

(Source: Google Search Off the Record Podcast)

Bing adds more new AI visibility reporting features to Webmaster Tools

Microsoft has introduced four new metrics in Bing Webmaster Tools: Intents, Topics, Citation Share, and Compare. The update builds on its existing AI Performance report, giving site owners more  detailed insight into how content is surfaced and cited in AI-generated answers.

Intents and Topics group queries into broader semantic categories, while Citation Share measures how often a site is cited relative to competitors. The changes  help formalise AI search optimisation as a measurable layer of SEO.

(Source: Bing)

Headline formats have minimal impact on Google Discover performance

Analysis of over 3.4 million articles indicates that common assumptions around headline structure—whether they’re formatted as questions, statements or quotes—do not reliably drive Discover visibility alone. Instead, performance metrics are more strongly tied to broader factors like content quality and topical relevance. The findings challenge the growing popularity of  ‘headline hacks’ as an optimisation strategy.

(Source: Search Engine Land)

Did you know you can now optimize for AI search and LLM mentions with Lumar?

Lumar’s GEO tools for AI search optimization provide data-driven insights to help you optimize your website for AI-driven search visibility, earn more AI citations, and improve brand visibility across LLMs.

Explore Lumar GEO Platform Features

Get started with Lumar

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The post AI Search & SEO Industry News – July 2026 appeared first on Lumar.

From crawl reports to AI Visibility scores, your Lumar projects are home to valuable website intelligence. MCP makes that information easier to explore, letting you ask questions in plain English and get clear answers directly from it — without switching tabs or copying data into a separate chat window.

This means less time pulling information together manually, and more time understanding what needs attention and deciding what to do next.

Here are seven (quick) ways to get started:

7 things MCP lets you do in minutes, not hours

1. Run an automated crawl audit

Working through reports tab by tab takes time.

With MCP, you can ask your AI assistant to audit your latest crawl. It can pull your health score, rank your biggest issues by volume, and compare what’s changed since last time — all in one reply.

AI retrieval systems don’t just reward semantic relevance—they reward structured relevance and logical reasoning pathways. 

2. Do your analysis in plain English

Finding the data you need often means building filters, writing queries, or exporting data for further analysis. 

Simply ask for what you need in plain English. Your assistant can filter and analyze any crawl summary or Lumar report directly inside the LLM environment you already work in.

3. Speed through admin and project setup

Segments, tasks, and custom metrics often mean clicking through forms. Chain them together, and you’re navigating multiple parts of the platform.

Instead, describe the outcome you want and let your assistant handle the process.

For example, you might want to create a custom metric that identifies a page template on your site, then build a segment based on that metric. 

Previously, this was a two-stage job across two parts of the platform. Now, it becomes one request:

The same goes for one-off admin: trigger crawls, create remediation tasks, link issues to Jira, or manage project segments in a single sentence.

4. Cover every project at once

Managing multiple sites or clients? Ask once and get a clear view across all of them.

See which projects need your attention, how performance is changing over time, and whether crawls are running as expected. You can also prepare for your next stand-up or client check-in without manually pulling updates from each project.

5. Manage your AI Visibility

As AI search continues to evolve, understanding where your brand appears in AI-generated answers is becoming increasingly important.

Make it a weekly habit. Ask your assistant how your AI Visibility has changed, review your latest prompt results, and understand how your topics and providers are performing. 

You’ll also get to see where your competitors are gaining ground:

6. Connect Lumar data with your other tools 

This is where it really comes together. MCP isn’t just for Lumar; it also lets you connect multiple services your teams rely on.

Connect them alongside Lumar, and your AI assistant can bring data and actions together in one conversation — reducing the need for exports, spreadsheets, and manual stitching.

Here are some more tools that pair well with Lumar:

• Google Search Console — match crawl issues to real impressions, clicks, and query data
• Google Analytics — weigh technical fixes against the traffic and conversions they affect
• Rank tracking tools (e.g. AccuRanker, SEMrush, Ahrefs) — connect ranking movement to the pages behind it
• Jira, Asana, or Linear — turn findings into tickets your dev team already works from
• Your CMS or data warehouse — cross-reference crawl data against content or business data
• Slack — post crawl summaries, receive alerts and post weekly wins to the channels your team already watches 

Ask one question and let your assistant do the piecing together:

That’s the kind of prioritized analysis that usually takes hours to pull together, now available from a single question.

7. Brief senior stakeholders in business language, not SEO jargon 

Your CMO does not want a list of canonical tags and redirect chains. They want to know what it means for traffic, revenue, and reputation. 

Ask your assistant to blend your crawl data (SEO) with your AI Visibility scores (GEO, generative engine optimization) and write the update for you, in language a non-specialist can act on. 

Try a prompt like:

In one reply you get a stakeholder-ready summary that: 

• Leads with the business headline: site health and AI search presence framed against traffic and revenue, not metric codes 
• Shows where GEO and SEO reinforce each other: the pages that both rank and earn citations in AI answers, and the technical issues putting that at risk 
• Answers the question every leader asks, “what are we not addressing?” : the topics and queries where your brand is absent from AI answers, ranked by opportunity 
• Ends with a short, plain-English next-steps list, with no SEO thinking required to read it

Add your Google Analytics or Search Console connector and the assistant can weight all of it by the traffic and revenue at stake, so the update lands with the people who hold the budget.

What is an MCP server?

MCP is an open standard that lets AI assistants like Claude, ChatGPT, Cursor, and others connect to external tools and data sources through a common interface.

By adding the Lumar MCP server to your AI assistant, you can ask questions, surface issues, and carry out actions without moving between tools.

MCP also lets you bring Lumar into connected workflows, combining Lumar data retrieval with actions like Jira ticket creation, Slack notifications, Google Search Console insights, and GitHub actions — all through one conversation.

The Lumar MCP server connects your AI assistant to two core areas of the platform:

Lumar Analyze — query crawl data, filter report rows, aggregate across dimensions, track health trends, manage tasks and segments, run exports and Single Page Requester jobs, and create custom metrics. Connect to Jira to raise issues straight from your findings.

Lumar AI Visibility — track visibility scores over time, see which topics and prompts drive citations, compare your brand against competitors in AI search, spot coverage gaps, and trigger fresh content-evaluation runs.

Setting up the Lumar MCP server

Setup takes about a minute. There are no API keys or config files to manage — add Lumar as a connector and log in.

What you’ll need

• A Lumar account
• An AI assistant that supports MCP connectors (Claude, ChatGPT, and others)

The three steps

1. Add a new connector in your AI assistant. The exact name varies by platform — look for Connectors, Integrations, or MCP servers in your settings.
2. Enter the Lumar server URL: https://mcp.lumar.io/mcp
3. Log in when prompted. Your assistant opens a Lumar login window. Sign in to authenticate the connection, and you’re done — the Lumar tools are now available in your chat.

Because you log in with your own Lumar account, there’s nothing to copy, store, or rotate. Authentication is handled for you.

Step-by-step setup by platform

Claude (claude.ai)

Plans that support MCP connectors: Free, Pro, Max, Team, and Enterprise. Free users are limited to one custom connector.

On Free, Pro, or Max:

1. Open claude.ai and go to Customize → Connectors.
2. Click the + button and select Add custom connector.
3. Enter a name (e.g. “Lumar”) and paste the server URL: https://mcp.lumar.io/mcp
4. Click Add, then log in to your Lumar account when prompted to complete authentication.

On Team or Enterprise:

An Owner must add the connector once for the organisation before members can use it.

1. Go to Organisation Settings → Connectors → Add → Custom → Web.
2. Enter the name and URL: https://mcp.lumar.io/mcp
3. Click Add, then authenticate with your Lumar account.

Once the Owner has added Lumar, each team member can connect their own account. Go to Customize → Connectors, find Lumar in the list, and sign in with your Lumar account.

ChatGPT

Plans that support MCP: Plus, Pro, Team, Business, Enterprise, and Education. Not available on the free plan.

Full write/action support (creating tasks, triggering crawls, managing segments) is available on Business, Enterprise, and Education plans. Plus and Pro users can connect and run read operations via Developer Mode, but write capabilities may be restricted.

Note: As of December 2025, ChatGPT renamed connectors to apps in their UI. If you see “Apps” where these instructions say “Connectors”, they’re the same thing.

1. Go to Settings → Advanced → Developer Mode and enable it.

2. Go back to Settings → Advanced and click Create App

3. Select Custom and enter the URL: https://mcp.lumar.io/mcp

4. Log in to your Lumar account when prompted.

Once connected, the Lumar app appears in your tools list. Select it at the start of a conversation to activate it.

Enterprise and Education plans: Admins manage connector setup via the workspace settings. Contact your ChatGPT workspace admin to request the Lumar connector be added.

Cursor

Plans: MCP is available on all Cursor plans.

1. Open Cursor and press Cmd+, (Mac) or Ctrl+, (Windows) to open settings.
2. Navigate to Tools & MCPs → MCP in the sidebar.
3. Click + Add New MCP Server.

4. Fill in the dialog:

• Name: Lumar
• Type: “http”
• URL: https://mcp.lumar.io/mcp

5. Click Save. You will be prompted to authenticate the MCP server — a green dot next to the server name confirms it’s running.

6. A browser window will open for you to log in to your Lumar account and authorise the connection.

To verify it’s working, open a new Composer session in Agent mode and ask: “What Lumar tools do you have access to?”

Other MCP-compatible tools

The Lumar MCP server uses the standard Streamable HTTP transport, so it works with any MCP-compatible tool. In all cases the server URL is: https://mcp.lumar.io/mcp

Look for the option to add a remote MCP server or custom connector in your tool’s settings. Enter the URL above, then authenticate the connection with your Lumar account when prompted.

If you’re unsure where to find your connector settings, check your tool’s documentation.

Important things to know

Connect at your own risk — and check your AI provider’s data policy first.

When you connect the Lumar MCP server, your crawl data passes through your chosen AI assistant to answer your questions. Depending on that provider’s terms, your data may be used to train their AI models. Lumar can’t control how a third-party AI platform handles data once it leaves our server.

Before connecting, only use an account and plan where the provider explicitly states your data won’t be used for training. This is typically the case on paid business, team, and enterprise tiers — but not always on free or individual plans. Check your provider’s data and privacy policy, and if you’re on a company account, clear it with your security or data team first.

Your Lumar permissions still apply. You authenticate by logging into your own Lumar account, so the connection only ever accesses data you’re already permitted to see.

Some actions make changes. Creating tasks, triggering crawls, and managing segments are write operations. Your assistant shows you what it’s about to do before acting — review before confirming.

Results reflect your latest crawl. The server queries live data. If a crawl is in progress, some reports may be incomplete until it finishes.

Troubleshooting

The Lumar tools aren’t showing up 

Check the server URL is entered exactly as https://mcp.lumar.io/mcp, and that you completed the login step when prompted. Some assistants need a restart after adding a connector.

I’m getting authentication errors 

Your session may have expired. Remove the connector and add it again, logging into your Lumar account when prompted.

The assistant can’t find a project 

Use the project’s full name as it appears in Lumar, or ask it to list your projects first: “What Lumar projects do I have access to?”

Responses are slow 

Large exports and AI Visibility trend analysis can take a few seconds. That’s normal for data-heavy requests.

What’s next

• Lumar Analyze overview
• AI Visibility: how scoring works
• Lumar API reference

Have feedback on this article? Use the thumbs up/down below or contact support.

The post How to Get Started with the Lumar MCP Server appeared first on Lumar.

Each month, Lumar’s in-house tech SEO experts hand-pick some of the SEO industry’s top news stories from across the web to keep you up-to-date on all things SEO and website optimization.

For our May 2026 SEO & GEO/AEO news roundup, the top headlines include:  

• Google I/O 2026 brought the biggest Search overhaul in 25 years — Plus: AI Mode has surpassed one billion monthly users just one year after launch, with query volume hitting an all-time high last quarter.
• Google published its first official AI search optimization guide covering crawlability, content structure, and technical SEO foundations for AI Mode and AI Overviews.
• Google’s May 2026 core update began rolling out on May 21st — it’s the second core update of the year, running in parallel with ongoing AI feature expansion in Search.
• Google launched a cross-channel shopping cart experience across Search, Gemini, and YouTube — letting users save and manage products across platforms as part of a more agentic shopping journey.
• Google Analytics added a new AI assistant traffic channel in GA4 — automatically grouping visits from tools like ChatGPT, Gemini, and Claude under a dedicated ai-assistant medium.
• AI search is more likely to cite in-depth content — that’s according to Google’s Nick Fox at Google Marketing Live 2026.
• Google removed the last remaining FAQ rich results from Search — including the phasing out of FAQ reporting in Search Console, marking another step away from traditional rich result features.
• Snap and Perplexity ended their planned $400 million AI search integration deal — a reminder that AI search distribution partnerships inside major consumer platforms are far from guaranteed.
• OpenAI made GPT-5.5 Instant ChatGPT’s new default model — with improvements in factuality, image understanding, and the model’s ability to decide when to use web search to inform its generative responses.
• (and more, below!)

Dive into the details behind the SEO and GEO/AEO headlines this month below…

But first: check out the new AI visibility tools in Lumar

Know where you stand in AI answers. New AI Visibility tools in Lumar track your brand’s AI presence, sentiment, and citations across the AI models your buyers are using — and tell you exactly what to do about the gaps.

Get a Demo of AI Visibility Tracking in Lumar

May 2026 SEO & GEO/AEO News Roundup:

Google I/O 2026: Google unveils biggest Search overhaul in 25 years as AI Mode hits 1 billion monthly users 

The hotly anticipated Google I/O 2026 summit saw Google unveil what it calls the biggest transformation in Search for more than 25 years. Updates include a redesigned AI-first search experience that adds more AI features than ever before, from persistent conversational context to autonomous “information agents” designed to help complete tasks.

At the I/O event, Google announced that, just one year after its debut, AI Mode has surpassed one billion monthly users, with queries more than doubling every quarter since launch. Google also reported that query volume reached an all-time high last quarter — framing AI Mode not as a replacement for Search but as a driver of more searching, not less.

The Google Search updates announced at I/O 2026 stop short of making AI Mode the default for Google Search, but it does reinforce how far we’ve moved away from traditional link-based SERPs. Search is becoming increasingly reliant on these task-oriented AI experiences, the likes of which promise to make tracking and monitoring SEO performance a whole lot more complicated.

(Source: Google’s The Keyword Blog ; Google I/O Keynote Videos)

Google publishes AI search optimization guide

Google has published its first formal documentation on optimizing for AI, “Optimizing your website for generative AI features on Google Search.” The focus is naturally on Google-owned features such as AI Mode and AI Overviews; however, in theory much of it should be transferable to other AI systems.

Much like traditional search, the emphasis is put on creating helpful, structured, and easily accessible content. The document also highlights the importance of page clarity and crawlability, with strong technical SEO foundations continuing to prove significant.

(Source: Google Developers Blog)

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Want a more in-depth guide to AI search optimization strategies? Our 80-page GEO / AEO playbook has you covered.

Google introduces cross-channel “universal” shopping carts

Google has announced a new shopping cart experience that works across platforms such as Search, Gemini and YouTube. For the first time, users can save, track, and manage products in one place while navigating across different apps and products.

The feature is designed to support more agentic shopping journeys, with AI surfacing price changes, availability updates, and product recommendations. It reinforces Google’s shift towards an ongoing AI-assisted shopping experience, rather than a series of isolated searches.

Per Google’s Universal Cart announcement:

“Universal Cart is an intelligent shopping cart and your new hub for shopping on Google. It works across merchants and across services, so you can add things to your cart while you’re browsing Search, chatting with Gemini, watching YouTube or even reading your Gmail.”

“The moment you add a product to your cart, it gets to work in the background — finding deals and price drops, giving you insights on price history and alerting you when an item is back in stock. It all runs on our Gemini models, so your cart gets even smarter as the models improve.”

(Source: Google’s The Keyword Blog)

Google releases data on how people are using AI Mode

A year on from the full launch of AI Mode in the US, Google has published data on its usage. The report shows that users are increasingly treating AI Mode differently to traditional search, leaning heavily on its ability to process complex, multi-layered queries.

Searches in AI Mode tend to be more exploratory in nature, with users asking follow-up questions and sometimes engaging in lengthy conversations with the bot. For site owners, it’s less about ranking for specific queries and more about ensuring your brand is visible in AI-driven systems.

Per the Google publication, “How people are using AI Mode in the US”: 

“AI Mode isn’t just changing how people search — it’s expanding the very definition of what’s searchable.”

(Source: Google)

Google launches its May 2026 core update

Google officially announced the rollout of the May 2026 core update on May 21, 2026. Google described it as “a regular update designed to better surface relevant, satisfying content for searchers from all types of sites,” and the rollout is expected to take approximately two weeks to complete. The update is global, affecting all regions and languages.

The May core update is Google’s second of 2026. As usual, no specific focus has been publicly stated for this core update, but its timing is interesting given the ongoing volatility caused by the expansion of AI features in Search. Both occurring in parallel will make it harder to separate algorithmic ranking shifts from broader AI-driven traffic changes.

(Source: Search Engine Journal)

Google Analytics adds new AI assistant traffic channel

Google has introduced a new AI assistant traffic category in GA4 that automatically groups visits coming from recognized AI tools such as ChatGPT, Gemini, and Claude.

The update assigns a new ai-assistant medium, putting this traffic into a dedicated default channel group. The goal is to make it easier for site owners to identify and compare AI-driven visits against traditional sources like organic search and direct traffic.

(Source: SEO Roundtable)

Google drops remaining FAQ rich results from Search

Google has removed any remaining FAQ rich results from appearing in Search. The feature has already been heavily restricted for most websites since 2023. The change also includes the phasing out of FAQ reporting in Search Console, with additional tooling support expected to be removed later in the year. 

FAQ structured data remains valid, but no longer produces visible SERP enhancements. The news marks another step in Google’s gradual reduction of traditional rich result features in favor of AI-driven experiences.

(Source: Search Engine Land)

AI search rewards content that goes deeper, says Google’s Nick Fox at Google Marketing Live 2026

In a Semafor interview about the future of AI search mechanics at this month’s 2026 Google Marketing Live event, Google’s Nick Fox (SVP of Knowledge & Information) emphasized that the proliferation of generative AI summaries heightens the algorithmic premium on “deep content.” Fox noted that while AI answers handle simple informational queries efficiently, they can’t replace rich, original human perspectives. As user behavior trends toward longer, more conversational queries, the AI systems seeks out primary evidence from external sources.

Fox’s advice for GEO/AEO? In the interview (around the 15-minute mark), he says:

“The biggest piece of advice we give is that the way to optimize for AI search is the same way to optimize for search: create great content that, as users, you would want. The additional piece of advice we give is: go beyond the surface level.”

“If you assume that the AI will provide sort of a first-level response or high-level framing, the content that will do the best within AI is one that goes one level deeper [or] two levels deeper and is really helpful.”

   
For content marketers and SEO pros, the takeaway is clear: Google continues to actively optimize its ranking systems to prioritize unique information that AI models cannot easily replicate. Content that relies on generic synthesis faces declining visibility, while firsthand data, unique brand perspectives, and expert analysis form the foundation for visibility in the AI era.

Watch the interview from Google Marketing Live 2026 here:

(Sources: Semafor ;  Search Engine Land ; Google Marketing Live 2026 interview video)

Snap ends its planned $400M Perplexity AI search deal

TechCrunch reported earlier this month that Snap and Perplexity “amicably ended” a previously announced $400 million deal that would have integrated Perplexity’s AI search engine into Snapchat. The partnership had been positioned as a way to bring conversational answers directly into Snapchat’s chat interface, but Snap said the companies had not agreed on a path to broader rollout.

For AI search watchers, the reversal is a useful reminder that distribution deals will be a major battleground — but not every AI search partnership will stick. Perplexity’s answer-engine model remains highly relevant to GEO/AEO, yet this update shows that embedding AI search into large consumer platforms can be commercially and operationally complicated. For brands, the broader lesson is to track not only standalone AI search products, but also where those products gain or lose distribution inside social, browser, and app ecosystems.

(Source: TechCrunch)

OpenAI releases GPT-5.5 Instant as ChatGPT’s new default model

This month, OpenAI announced that GPT-5.5 Instant will now be ChatGPT’s new default model, saying it is designed to provide smarter, clearer, more accurate, and more personalized responses. OpenAI also highlighted improvements in factuality, image understanding, STEM questions, and the model’s ability to decide when to use web search.

Per the OpenAI announcement:

“GPT‑5.5 Instant is a generally smarter model that’s more capable across everyday tasks, including improvements in analyzing photo and image uploads, answering STEM-related questions, and deciding when to use web search to provide a more useful answer.”

For GEO and AI search practitioners, the “when to search the web” piece is particularly important. As ChatGPT becomes better at deciding when fresh external information is needed, the relationship between model behavior, source retrieval, and content citation becomes more strategically important. Brands that want to be discovered in ChatGPT environments need to consider whether their content is accessible, authoritative, up to date, and useful enough to be retrieved when the model determines that web grounding is needed.

(Source: OpenAI announcement)

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The post AI Search & SEO Industry News – May 2026: Google I/O, the May Core Update, and More appeared first on Lumar.

Your content needs to build a case, not just make a claim

AI retrieval systems don’t just reward semantic relevance—they reward structured relevance and logical reasoning pathways. 

Content that clearly signals its intent, preserves entity and entity relationship clarity across sections, and builds connected reasoning paths is more resilient in AI retrieval environments and more likely to be incorporated into generated answers.

The idea that content structure can impact AI selection likelihood was a key finding in last year’s research paper, “What External Knowledge is Preferred by LLMs? Characterizing and Exploring Chain of Evidence in Imperfect Context for Multi-Hop QA” (Chang, et al.). 

This research on retrieval-augmented LLMs shows that AI models perform best and deliver more accurate responses when the external content being considered forms a coherent, logically structured “chain of evidence” (CoE) rather than a loose collection of related facts.

Characteristics of ‘Chain of Evidence’ (CoE) content

You can think of the Chain of Evidence (CoE) approach as a way to ensure your content does more than just mention the right topic. It should clearly connect the user’s question to the answer through a logical path of supporting information.

A CoE-aligned piece of content has three main characteristics:

1. Clear intent alignment: The content understands and addresses what the user is really trying to find out. It does not just match the keywords in the query; it addresses the answer type or outcome the user is looking for.
2. Strong evidence nodes: The content includes the key entities, concepts, or facts needed to answer the question. These are the “stepping stones” an AI system or reader needs to move from the query to the answer.
3. Explicit evidence relationships: The content makes the connections between those entities clear. It explains how one fact relates to another, rather than leaving the reader or AI system to infer the logic.

The paper’s authors demonstrate that when the external knowledge provided to the LLM at inference time is ‘noisy’ or contains lots of irrelevant information, AI models’ outputs are significantly more accurate when the retrieved content exhibits CoE characteristics.

So, when optimizing your content for GEO/AEO, you’re not just aiming to boost relevance alone. You also need to structure your relevant points so that the pieces of evidence relating to your claims support each other in a logical chain. 

How can CoE content influence GEO/AEO?

In a RAG environment where an AI system might pull 10 different snippets from 10 different websites, the one with the strongest “chain of evidence” reasoning structure is the one most likely to survive the “noise” and get cited in the final generated response.

When these structural content elements are present together, LLM model accuracy remains more stable—even as irrelevant or conflicting information increases. 

Structuring your content as a “chain of evidence” can also help preserve brand accuracy in AI-generated responses. Per the research paper, if the external knowledge the LLM is relying on to form its generative response exhibits these CoE characteristics, “it can better resist interference from extraneous and even inaccurate information.”

(Note: The researchers found that the same CoE content structures can also make incorrect information more persuasive to AI systems if it appears logically coherent. This suggests that content structure may influence AI reasoning, regardless of whether the facts are correct. This is why ‘LLM-as-a-judge’ content evaluation guardrails are particularly important in ensuring generative AI platforms can deliver the best possible responses to users — more on that in our “LLM-as-a-Judge: How to Become a Preferred Content Source for AI Answers” post.)

Optimizing content for AI search visibility and LLM citations isn’t just about answering one prompt. It’s about building content that creates a clear chain of evidence: connecting entities, claims, supporting facts, definitions, and context in a way that helps AI systems follow the reasoning and understand why your answer is trustworthy and complete.

GEO/AEO Tools

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The post Creating “Chain of Evidence” (CoE) Content for GEO / AEO appeared first on Lumar.

To be selected and cited by LLMs, a piece of content must first be unmistakably relevant to the query.

Semantic relevance refers to how closely your content matches the underlying meaning and intent of a user’s query — not just the exact keywords used in a prompt. (This is because, rather than exactly matching keywords in a user’s query to a piece of content, AI retrieval systems rely heavily on vector embedding-based semantic similarity and hybrid retrieval models to determine which passages are conceptually aligned with a user’s question.)

For generative engine optimization (GEO, also known as AEO), you need to think beyond keywords. Establishing broader semantic relevance and answer “completeness” in your content matters more than ever because AI search systems often perform a “query fan-out” when generating responses to a prompt. This means that the AI expands a single user question into multiple related sub-queries to gather broader context before generating an answer.

“AI often expands a single query into multiple sub-queries. Structuring content in tightly knit topical clusters ensures your brand becomes the authoritative source across the entire topic, capturing both the main question and all related follow-ups.”

— Stephen Akadiri, Senior SEO & Organic Growth Specialist

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For GEO, semantic relevance is more impactful than stylistic choices in content

In a paper by UC Berkeley academics, “What Evidence Do Language Models Find Convincing?”, researchers showed that LLMs tend to overrely on semantic relevance and ignore many stylistic features of text that humans often deem important when assessing a document’s credibility. The team’s experiments showed that when AI models are forced to choose between two conflicting paragraphs and instructed to use only the provided text, relevance signals dominate the AI’s reasoning.

The stylistic elements that appeared to be largely ignored by LLMs in this research included language aspects such as:

• a neutral/objective tone of voice 
• confident or assertive language 
• domain-specific terminology and technical terms 
• lexical diversity (rich vocabulary) 
• sentence length, or word complexity

What the AI models prioritize instead:

In contrast, the research found strong positive effects on LLM selection from:

• question–paragraph vector embedding similarity 
• n-gram overlap with the question 
• explicit relevance boosts (e.g., pre- fixing with: “The following text is about the question: [question]”)

These relevance-based modifications significantly increased paragraph win-rate. (Note, in the context of this experiment, a paragraph “wins” when the AI model’s generated answer aligns with that source paragraph’s position in a head-to-head comparison against another paragraph on the same topic.)

In short, simple relevance features correlated much more strongly with which evidence the AI model “believes” (and then serves to users) compared to the more stylistic elements of a text.
Which is to say that semantic relevance seems to provide the strongest influence on LLMs’ judging and selection.

Learn more about optimizing your content for AI search visibility in our introduction to Content GEO.

How to optimize your content for semantic relevance

Semantic relevance goes well beyond the simple keyword-matching SEO tactics of yesteryear; it means ensuring your content matches the user’s true intent behind the prompts they input into AI systems. By mirroring actual user language, comprehensively answering questions, and expanding on related concepts, you move beyond the old keyword-stuffing approach to become a more authoritative source for a given query in the AI search landscape.

Semantic relevance optimization checklist

□    Lead with the question in the reader’s words. Use the terms and near-synonyms that appear in real user queries that your content can help answer. 

□   Answer early, then justify. Put a direct “Yes/No/It depends” style thesis near the top, followed by supporting reasoning. 

□   Optimize for hybrid retrieval by combining lexical precision with semantic expansion. Lexical precision reinforces exact-term and chunk-level matching, while semantic breadth increases recall by covering how different users might phrase related queries. Introduce related terms intentionally to widen your retrieval footprint without weakening entity clarity. 

□   Use a tight topical focus per section. Avoid burying the answer inside unrelated background. Keep each section focused on one claim or sub-question to improve: passage-level retrieval, chunk-level assessment, AI citation precision, and featured snippet capture. (Think in terms of ‘retrievable modules’ of content.) 

□   Optimize for passage-level clarity. Use specific named entities within each passage instead of pronouns (it, they). This helps ensure the passage is “complete” contextually, even when pulled in isolation. 

□   Make content scope explicit. Time bounds, geographies, and scope definitions (“in 2026,” “in the UK,” “for healthy adults”) reduce ambiguity and help retrieval match accurately. 

□   Cover counterarguments explicitly. AI systems frequently summarize both sides of contentious topics. If you don’t address counterarguments, another source will. Include: what critics say on the topic, where evidence is weak, and what remains uncertain. This improves topical completeness, balanced retrieval likelihood, and resistance to one-sided citation bias.

Further Reading

Semantic Relevance & SERP Rankings

Learn more about how semantic relevance also impacts traditional SEO today in our eBook, “SEO Research Study: Semantic Relevance and Search Engine Rankings.”

Passage-level semantic relevance

Many AI systems retrieve and synthesize information at the passage (or “chunk”) level.  If your content is semantically relevant at the passage level, clearly signaling what each section or module of your content is about and keeping core entities clear throughout each passage, it may improve your chances of getting that passage cited. That said, there’s quite a bit of disagreement about “content chunking” approaches in GEO/AEO…

On the content chunking debate in GEO / AEO

While Google has said that content chunking is unnecessary for appearing in its own AI systems, it’s worth understanding the concept, why it’s an often-discussed tactic in GEO/AEO, and why optimizing content at the “chunk” level might still matter for visibility in other AI systems.

Content chunking & AI extractability

Many AI systems do not “read” your entire page at once during their information retrieval phase. Instead, they utilize a process called chunking — breaking documents into smaller, discrete segments that can be independently indexed and retrieved.

AI sometimes uses a content chunking method because LLMs often operate on ultra-large knowledge bases, containing more “tokens” of contextual data and content than can be easily included in a single prompt, thus requiring a more scalable RAG (Retrieval-Augmented Generation) system.

Per Anthropic:

“RAG works by preprocessing a knowledge base using the following steps:

• Break down the knowledge base (the “corpus” of documents) into smaller chunks of text, usually no more than a few hundred tokens; [*Editor’s Note: 100 tokens = roughly 75 words.] 
• Use an embedding model to convert these chunks into vector embeddings that encode meaning; 
• Store these embeddings in a vector database that allows for searching by semantic similarity.
• At runtime, when a user inputs a query to the model, the vector database is used to find the most relevant chunks based on semantic similarity to the query. Then, the most relevant chunks are added to the prompt sent to the generative model.”

Critically, the retrieval step for some AI systems operates at the passage level, not the page level — typically retrieving sections of 100-300 words that semantically match the query. 

Passage-level retrieval is common because it is often more efficient and relevant than sending an entire long document into a model for every query. LLMs have context window limits, so feeding an entire 3,000-word article into the model for every query would be computationally expensive and often irrelevant. Instead, many AI retrieval systems surface only the most relevant passages, which are then used as context for answer generation. This means your content’s internal structure can influence how easily individual sections are interpreted, retrieved, and reused by systems that operate at the passage level. A 3,000-word article might have ten different sections that could be independently retrieved for ten different queries — but only if each section is structured to stand on its own.

Semantic chunking

According to research from Anthropic (“Introducing Contextual Retrieval in AI Systems”), standard RAG (Retrieval-Augmented Generation) systems often fail when individual content chunks lack sufficient context, leading to “failed retrievals” or hallucinations:

“In traditional RAG, documents are typically split into smaller chunks for efficient retrieval. While this approach works well for many applications, it can lead to problems when individual chunks lack sufficient context.”

For GEO, this means you should move beyond “fixed-size” chunking (splitting content into smaller chunks purely by a set number of characters or length) in favor of “Semantic Chunking”; organizing your content into semantically complete, coherent modules. This strategy ensures that each section of your content represents a “complete thought” that an AI can extract and use without losing the surrounding logic, making it significantly easier for AI “Judges” to verify your evidence chain. 

With semantic chunking, you’ll want to avoid the “pronoun penalty,” or using pronouns instead of specific entity names in any given chunk of content. 

As explained in the Anthropic research:

“A relevant chunk might contain the text: ‘The company’s revenue grew by 3% over the previous quarter.’ However, this chunk on its own doesn’t specify which company it’s referring to or the relevant time period, making it difficult to retrieve the right information or use the information effectively.”

This suggests that, if you want to make it as easy as possible for AI retrieval systems to understand our content, you should keep core entities visible throughout your content ‘chunks’. Repeat key entity names and terms consistently so the reasoning chain is easy to follow across individual content sections.

While pronouns may improve human readability, excessive substitution of pronouns for core entities (e.g., replacing “Steve Jobs” with “he,” or “the company” with “it”) can reduce the visibility of those entities within a passage. In AI retrieval systems that evaluate passages based on entity presence and relational clarity, this can weaken alignment with the query’s underlying reasoning structure.

While the Anthropic research on contextual retrieval for RAG systems was actually written to introduce a solution to this information chunk retrieval issue (“Contextual Retrieval solves this problem by prepending chunk-specific explanatory context to each chunk before embedding”), and while Google says content chunking is unnecessary for visibility its own AI systems, you still might want to consider semantically chunking your content as a GEO best practice to help your content maintain utmost ease of retrieval across all AI systems, regardless of how sophisticated or contextually aware their RAG systems may be.

GEO and SEO experts still routinely echo the suggestion to optimize your content into semantically coherent chunks. After studying AI patents and research papers for two years, SEO and AI search thought leader Olaf Kopp wrote in a 2026 LinkedIn post that one of his key takeaways is:

“Structure content as self-contained, answer-first chunks so LLMs can find, extract, and cite precise information.”

Content chunking isn’t about gaming retrieval algorithms. It’s about recognizing that if some AI systems will only “see” one section of your article at a time, that section needs to be independently valuable and comprehensible.

This doesn’t mean arbitrarily or awkwardly breaking content up into fixed-length smaller parts. What you need is clear sectioning, for both human readers’ comprehension and information gain and machine-readability: use H2 and H3 headings to organize content into logical sub-topics, ensure each section addresses a distinct aspect of your subject, and write each section so it can be understood without requiring the reader (or the AI) to have read what came before.

After all, Google is not saying that content structure is irrelevant. Common, longstanding SEO and general writing advice also recommends organizing content into clear paragraphs, sections, and headers for human readers, too. Clear structure is good SEO, good UX, and likely helpful for some AI systems’ information retrieval processes.

In short: You probably don’t need to artificially “chunk” content into rigidly imposed, limited-length sections just because someone says LLMs prefer tiny blocks (especially for Google’s AI systems). But do structure and segment content clearly because readers, crawlers, accessibility tools, snippets, and many AI retrieval systems all benefit from passage-level clarity.

In other words: don’t “chunk” content purely as a GEO hack. Write clear sections for humans that are also easy for retrieval systems to understand.

About Lumar’s GEO / AEO explainer series

In this Lumar series, we’re exploring strategies for generative engine optimization (GEO), also known as answer engine optimization (AEO) — that is, how to boost your brand’s AI visibility and likelihood of earning mentions or citations from LLMs and AI-powered platforms like ChatGPT, Claude, Gemini, Perplexity, or Google’s AI Overviews and AI Mode.

Free GEO / AEO eBook

Get the full GEO/AEO guide

Reminder: This article is an excerpt from our in-depth guide to AI search optimization. Get the FULL 80-page AI search playbook (it’s free!) here.

The post Semantic Relevance for GEO / AEO: How to Align Content with AI Search Intent appeared first on Lumar.

While Google has said that content chunking is unnecessary for appearing in its own AI systems, it’s worth understanding the concept, why it’s an often-discussed tactic in GEO/AEO, and why optimizing content at the “chunk” level can still matter for visibility.

Content chunking & AI extractability

Many AI systems do not “read” your entire page at once during their information retrieval phase. Instead, they utilize a process called chunking — breaking documents into smaller, discrete segments that can be independently indexed and retrieved.

AI sometimes uses a content chunking method because LLMs often operate on ultra-large knowledge bases, containing more “tokens” of contextual data and content than can be easily included in a single prompt, thus requiring a more scalable RAG (Retrieval-Augmented Generation) system.

Per Anthropic:

“RAG works by preprocessing a knowledge base using the following steps:

• Break down the knowledge base (the “corpus” of documents) into smaller chunks of text, usually no more than a few hundred tokens; [*Editor’s Note: 100 tokens = roughly 75 words.] 
• Use an embedding model to convert these chunks into vector embeddings that encode meaning; 
• Store these embeddings in a vector database that allows for searching by semantic similarity.
• At runtime, when a user inputs a query to the model, the vector database is used to find the most relevant chunks based on semantic similarity to the query. Then, the most relevant chunks are added to the prompt sent to the generative model.”

Critically, the retrieval step for some AI systems operates at the passage level, not the page level — typically retrieving sections of 100-300 words that semantically match the query. 

Passage-level retrieval is common because it is often more efficient and relevant than sending an entire long document into a model for every query. LLMs have context window limits, so feeding an entire 3,000-word article into the model for every query would be computationally expensive and often irrelevant. Instead, many AI retrieval systems surface only the most relevant passages, which are then used as context for answer generation. This means your content’s internal structure can influence how easily individual sections are interpreted, retrieved, and reused by systems that operate at the passage level. A 3,000-word article might have ten different sections that could be independently retrieved for ten different queries — but only if each section is structured to stand on its own.

Semantic content chunking

According to research from Anthropic (“Introducing Contextual Retrieval in AI Systems”), standard RAG (Retrieval-Augmented Generation) systems often fail when individual content chunks lack sufficient context, leading to “failed retrievals” or hallucinations:

“In traditional RAG, documents are typically split into smaller chunks for efficient retrieval. While this approach works well for many applications, it can lead to problems when individual chunks lack sufficient context.”

For GEO, this means you should move beyond “fixed-size” chunking (splitting content into smaller chunks purely by a set number of characters or length) in favor of “Semantic Chunking”; organizing your content into semantically complete, coherent modules. This strategy ensures that each section of your content represents a “complete thought” that an AI can extract and use without losing the surrounding logic, making it significantly easier for AI “Judges” to verify your evidence chain. 

Learn more about how semantic relevance impacts AI search results in our recent GEO/AEO explainer article.

Keeping entities clear across sections

With semantic chunking, you’ll want to avoid the “pronoun penalty,” or using pronouns instead of specific entity names in any given chunk of content. 

As explained in the Anthropic research:

“A relevant chunk might contain the text: ‘The company’s revenue grew by 3% over the previous quarter.’ However, this chunk on its own doesn’t specify which company it’s referring to or the relevant time period, making it difficult to retrieve the right information or use the information effectively.”

This suggests that, if you want to make it as easy as possible for AI retrieval systems to understand our content, you should keep core entities visible throughout your content ‘chunks’. Repeat key entity names and terms consistently so the reasoning chain is easy to follow across individual content sections.

While pronouns may improve human readability, excessive substitution of pronouns for core entities (e.g., replacing “Steve Jobs” with “he,” or “the company” with “it”) can reduce the visibility of those entities within a passage. In AI retrieval systems that evaluate passages based on entity presence and relational clarity, this can weaken alignment with the query’s underlying reasoning structure.

While the Anthropic research on contextual retrieval for RAG systems was actually written to introduce a solution to this information chunk retrieval issue (“Contextual Retrieval solves this problem by prepending chunk-specific explanatory context to each chunk before embedding”), and while Google says content chunking is unnecessary for visibility its own AI systems, you still might want to consider semantically chunking your content as a GEO best practice to help your content maintain utmost ease of retrieval across all AI systems, regardless of how sophisticated or contextually aware their RAG systems may be.

GEO and SEO experts still routinely echo the suggestion to optimize your content into semantically coherent chunks. After studying AI patents and research papers for two years, SEO and AI search thought leader Olaf Kopp wrote in a 2026 LinkedIn post that one of his key takeaways is:

“Structure content as self-contained, answer-first chunks so LLMs can find, extract, and cite precise information.”

Content chunking isn’t about gaming retrieval algorithms. It’s about recognizing that if some AI systems will only “see” one section of your article at a time, that section needs to be independently valuable and comprehensible.

This doesn’t mean arbitrarily or awkwardly breaking content up into fixed-length smaller parts. What you need is clear sectioning, for both human readers’ comprehension and information gain and machine-readability: use H2 and H3 headings to organize content into logical sub-topics, ensure each section addresses a distinct aspect of your subject, and write each section so it can be understood without requiring the reader (or the AI) to have read what came before.

After all, Google is not saying that content structure is irrelevant. Common, longstanding SEO and general writing advice also recommends organizing content into clear paragraphs, sections, and headers for human readers, too. Clear structure is good SEO, good UX, and likely helpful for some AI systems’ information retrieval processes.

In short: You probably don’t need to artificially “chunk” content into rigidly imposed, limited-length sections (especially for Google’s AI systems). But do structure and segment content clearly because human readers, crawlers, accessibility tools, and many AI retrieval systems all benefit from passage-level clarity.

About Lumar’s GEO / AEO explainer series

In this Lumar series, we’re exploring strategies for generative engine optimization (GEO), also known as answer engine optimization (AEO) — that is, how to boost your brand’s AI visibility and likelihood of earning mentions or citations from LLMs and AI-powered platforms like ChatGPT, Claude, Gemini, Perplexity, or Google’s AI Overviews and AI Mode.

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The 2026 AI Search Playbook

Get our full, FREE guide to GEO / AEO strategies now.

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The post Content Chunking & AI Extractability appeared first on Lumar.

Which external content sources are preferred by AI?

An academic paper updated last year (“From Generation to Judgment: Opportunities and Challenges of LLM- as-a-Judge”) surveyed the emerging field of ‘LLM judges’ (that is, AI systems built to evaluate, score, and ‘judge’ inputs — or their own outputs). One thing is clear from the research: AI systems are getting increasingly good at content evaluation.

Understanding how LLMs are currently used for evaluation across various use cases — and what criteria they excel (or fail) at assessing — can give us some insights into how AI platforms may evaluate and select citations from external content (like yours!).

AI researchers (see sources under ‘Further Reading’ below) have found that LLMs prefer content that is:

• Relevant to the questions users are asking 
• Coherent, well-evidenced, and logically structured 
• Validated by others (ie, highly cited or frequently mentioned by others) 
• Includes credible citations to others, substantiating its claims and trustworthiness
• “Helpful, honest, and harmless” (See notes on Claude’s ‘Constitution’, below)
• Complete in its topical coverage
• Recent 
• Easily extractable

“We need to understand how AI interprets our content and generates answers so we can adapt our strategies and ensure our content appears in results.”

— Ana Perez, SEO Manager

Why content quality matters more than ever in the age of AI search

Your content quality matters in AI search because generative systems rely heavily on retrieval signals, source reputation, and structured knowledge to decide which content to incorporate into their answers. Modern AI-powered search experiences do not generate responses from thin air — they draw from indexed web content, ranked sources, and retrieval-augmented pipelines. 

When determining which external sources to cite, summarize, or rely on, AI systems draw on many of the same credibility signals that underpin traditional ranking systems: domain authority, consistent authorship, factual alignment, and corroboration across trusted sources. 

In practice, high E-E-A-T (experience, expertise, authority, and trustworthiness) content is more likely to be retrieved, retained in context, and considered safe to reuse in AI-generated answers.

SEO professionals will already be very familiar with the concept of E-E-A-T. The idea stems from Google’s internal Search Quality Rater Guidelines, the massive manual used by thousands of human testers to grade Google’s search algorithm.

First popularized as a benchmark for “Your Money or Your Life” (YMYL) topics, E-E-A-T has evolved from a niche set of quality guidelines into a foundational pillar of how search algorithms identify and reward the most credible voices in any given field. 

E-E-A-T can also serve as a strong content framework for GEO/AEO, as AI search systems have an incentive to prioritize factually accurate, credible, trustworthy content in order avoid the reputational damage caused by LLM hallucinations (we’ve all seen plenty of examples of this in the press over the past few years!).

On content quality in the age of AI search:

“Content quality is always going to be a trending topic, but even more so in 2026. Brands that rely too heavily on AI to write copy will struggle to demonstrate genuine E-E-A-T. Google and OpenAI’s models are already learning to evaluate who is behind the content, why it exists, and what value it adds, so I would say content quality should be on everyone’s radar! “

—Chloe Steele, SEO Account Manager at Verde Digital

(Hear more from Chloe in our Lumar webinar session: “SEO & GEO Trends for 2026”) 

AI’s grading rubric: how LLMs can evaluate content quality today

To understand why some content is cited and others ignored, we must look at how AI evaluates quality. 

The LLM-as-Judge paper we mentioned earlier outlines the primary benchmarks that today’s AI systems can use to “judge” external inputs as well as their own outputs.

For a piece of content to survive the AI’s internal filtering process, it should score high across six critical areas:

• Helpfulness 
• Harmlessness
• Relevance 
• Reliability 
• Feasibility, and
• Overall Quality

These aren’t just abstract concepts; they are specific metrics an AI “judge” might use to decide if your content is authoritative enough to present to a user.

But are these AI-powered evaluations actually being implemented by public-facing AI platforms today? Yes, it seems so.

Anthropic’s Claude 2 Model Card, for example, discusses their work to train Claude on the three H’s; they have explicitly stated that they are training their LLM to be Helpful, Honest, and Harmless.

Per the Claude 2 Model Card document:

“Our core research focus has been training Claude models to be helpful, honest, and harmless. Currently, we do this by giving models a Constitution – a set of ethical and behavioral principles that the model uses to guide its outputs.”

“You can read about Claude 2’s principles in a blog post we published in May 2023 [See: Claude’s Constitution]. . . . We use the constitution in two places during the training process. During the first phase, the model is trained to critique and revise its own responses using the set of principles and a few examples of the process. During the second phase, a model is trained via reinforcement learning, but rather than using human feedback, it uses AI-generated feedback based on the set of principles to choose the more harmless output.”

Industry analysis of Google’s documentation suggests that its AI Overviews are not a fully separate silo; they are powered in part by some of the same core ranking systems as traditional search, including the “Helpful Content System.” If content is deemed “helpful” and demonstrates strong E-E-A-T according to Google’s search evaluation criteria, chances are, it’s also more likely to be considered a valuable source for AI summarization.

When it comes to other companies’ AI search tools, like those embedded in Microsoft Bing, we also have clues that these AI systems are being developed to prioritize trustworthy content. In Microsoft’s “Responsible AI” document, it underscores Microsoft’s commitment to “Fairness,” “Reliability & Safety,” and “Transparency” in its AI systems. For Bing’s Copilot to be reliable, safe, and transparent, the sources it uses for information should be demonstrably accurate and trustworthy.

Likewise, in Perplexity’s help articles, it states that: “When you ask Perplexity a question, it uses advanced AI to search the internet in real-time, gathering insights from top-tier sources.” — Again, content quality and E-E-A-T signals are at work here.

“SEO best practices remain essential even as AI search grows. Focus on building brand visibility across multiple channels, creating AI-friendly content with structured headings and quotable insights, researching real user questions, and incorporating first-party data and unique perspectives. Above all, make your content genuinely helpful.”

— Ana Perez, SEO Manager

How to improve EEAT for GEO

As AI-generated content floods the web, human expertise is becoming a differentiating factor rather than a baseline expectation. LLMs are increasingly trained to recognize who produced content, why it exists, and what real-world experience backs it up. Surface-level content is increasingly filtered out in favor of genuinely authoritative sources.

As Jon Clark, Managing Partner at Moving Traffic Media, puts it:

On taking E-E-A-T to the next level:

“With all this AI-generated content spilling out onto the web, human expertise is about to become a major differentiator. Again. It’s the un-apologetic authors, the credentials that stand up to scrutiny and the real hands-on experience that sets the regurgitated reviews and surface-level tat apart from genuine authority.

[In] 2026, both Google and the AI systems they’re building will be looking for the all-important trust credentials – where did that insight come from, and who put their name on it.”

• (Note: You can hear more from Jon in his Lumar GEO/AEO webinar session, “The New Metrics of AI Search – GEO/AEO KPIs You Should Track Now.” )

Get EEAT Analytics in Lumar

Our AI Authority / E-E-A-T reports in Lumar can help you ensure your content signals expertise, experience, authority, and trustworthiness. 

Get a Lumar GEO platform demo to see our E-E-A-T reports in action— alongside our many other GEO/AEO reporting and analytics tools, like our GEO Content Evaluator.

Content Quality Checklist for GEO / AEO

Practically, creating high-quality, strong E-E-A-T content for generative engine optimization means incorporating the following into your content production workflows:

□   Named authorship with verifiable credentials — Content attributed to real experts with demonstrable experience signals trust to both human readers and AI systems evaluating source quality.

□  First-party data and original research — Proprietary statistics, surveys, and case studies give AI systems unique, citable material that they cannot source elsewhere.

□  Factual accuracy and consistency — Contradictions between pages on your own site, or between your content and established facts can erode AI trust in your brand as a reliable source.

□   Transparent sourcing — Citing reputable external references, studies, and named experts reinforces the factual grounding of your content.

New Lumar tools for GEO content optimization

Explore Lumar’s powerful platform features for GEO content optimization:

• GEO Content Evaluation Tools
• AI Visibility Tracking Tools
• The Full Lumar GEO / AEO Platform

Get Lumar’s FULL guide to GEO / AEO

Ready to dive deeper into how to build a strong GEO / AEO strategy for AI search? Get our full, free “AI Search Optimization Playbook” now.

The Ultimate Guide to GEO

Get the full GEO/AEO playbook

This post is an excerpt to our 80-page guide to GEO / AEO – Get the FULL AI Search Playbook for free here.

About Lumar’s GEO / AEO explainer series

In this Lumar series, we’re exploring strategies for generative engine optimization (GEO), also known as answer engine optimization (AEO) — that is, how to boost your brand’s AI visibility and likelihood of earning mentions or citations from LLMs and AI-powered platforms like ChatGPT, Claude, Gemini, Perplexity, or Google’s AI Overviews and AI Mode.

The post LLM-as-a-Judge: How to Become a Preferred Content Source for AI Answers appeared first on Lumar.

An introduction to Content GEO

Content GEO is about ensuring your content is built for both human understanding and AI retrieval, assessment, selection, and inclusion. It requires focus on publishing factually accurate, well-structured, semantically relevant, and comprehensive content that AI systems can extract, summarize, and cite confidently. 

For human readers, your content still needs to be clear, useful, accurate, persuasive, and easy to navigate. 

For AI systems, your content also needs to be structured in a way that makes its meaning, relevance, evidence, and expertise easy for machines to identify.

That means Content GEO is not about writing robotic copy or chasing GEO/ AEO “hacks.” It is about making your best expertise easier for machines to understand without making it less useful for people.

A good Content GEO strategy should address questions like:

• Is this content clearly about the topic it claims to cover?
• Does it answer the user’s question directly?
• Does it contain accurate, specific, and useful information?
• Does it show why the brand is qualified to speak on this topic?
• Can an AI system extract a useful answer from this page without misrepresenting it?
• Does the content strengthen the brand’s association with the topics it wants to own?

If Technical GEO determines whether your content is accessible to AI bots, Content GEO determines whether it is deemed useful, trustworthy, and likely to be chosen. It ensures that once an AI system can access your content, recognize your brand, and trust your authority (see also: Entity GEO and Brand Authority GEO), it has the right material to use when generating its responses for users.

Why It Matters Now: As AI-generated “slop” floods the web, LLMs are being trained to prioritize “helpful, honest, and harmless” content (the 3 Hs!). To survive the AI’s internal filtering process, your content must score high in helpfulness, reliability, and overall quality.

The Ultimate Guide to GEO

Get the full GEO/AEO playbook

This post is a companion piece to our 80-page guide to GEO / AEO – Get the FULL AI Search Playbook for free here.

Differences between SEO content and GEO content

Where traditional SEO content aimed to align with keyword queries, Content GEO focuses on satisfying AI reasoning — ensuring your brand’s insights, explanations, and expertise become the material LLMs rely on when generating answers.

In an AI-driven search landscape, your content adds yet another dimension to its role in your broader marketing toolkit; it becomes training material for the models shaping the future of online search.

“We need to understand how AI interprets our content and generates answers so we can adapt our strategies and ensure our content appears in results.”

— Ana Perez, SEO Manager

Traditional SEO content has often been built around ranking for a keyword and earning the click. Content GEO expands that goal.

In AI search, visibility may happen before, after, or without a website visit. Your content might influence an AI-generated answer even when the user does not click. Your brand might be cited directly, mentioned without a link, summarized as part of a recommendation, or omitted entirely.

That means content teams need to think beyond traffic alone. Content can now shape:

• AI understanding: Whether AI systems correctly understand what your brand knows, offers, and stands for.
• AI inclusion: Whether your content is selected as part of an AI-generated response.
• AI citation preference: Whether your content is chosen over a competitor’s when multiple sources could answer the same question.
• Brand-topic association: Whether AI systems consistently connect your brand with the topics that matter to your category.
• Answer accuracy: Whether AI systems describe your brand, products, services, and expertise correctly.

This does not make traditional SEO obsolete. Strong SEO fundamentals still matter. Many AI search experiences rely on traditional search indexes, established content signals, and web-wide authority signals. But Content GEO asks content teams to build pages that are not only optimized for ranking and reading, but also for retrieval and reuse.

Content SEO and Content GEO overlap, but they are not identical.

Traditional content-focused SEO tactics often start with a search query, keyword opportunity, ranking gap, or traffic goal. The main question is: How do we create the best page to rank and earn the click?

Content GEO starts with a slightly different question: How do we create the best source for an AI system to understand, trust, and use when generating an answer?

That shift changes the content brief.

A traditional SEO-focused content brief might emphasize target keywords, search volume, H1s, title tags, internal links, and SERP competitors. A GEO content brief still includes those considerations, but it also asks for clearer answers, more explicit definitions, stronger evidence, better topical alignment, visible expertise, and page sections (or ‘chunks’) that make sense when interpreted outside of the full page context.

The best Content GEO work does not abandon SEO. It raises the quality bar.

Where Content GEO fits in the AI visibility funnel

Content GEO sits primarily in the AI Understanding and AI Inclusion stages of the AI visibility funnel.

At the discovery stage, technical factors determine whether AI bots and search crawlers can access your content. That is the domain of Technical GEO.

But once your content can be accessed, the next questions are different:

• Can AI systems understand what this content is about?
• Can they connect it to the right topics and entities?
• Can they identify a useful answer?
• Can they evaluate the content as accurate, helpful, and trustworthy?
• Can they confidently include it in a generated response?

Those are Content GEO questions.

This is why Content GEO cannot operate in isolation. It works best when the other GEO pillars are already in motion. If AI cannot crawl your page, the content may not be discovered. If AI cannot recognize your brand as an entity, the brand may not be properly associated with the content. If AI does not see enough authority signals around your brand, even strong content may lose out to a more trusted source.

Content GEO is the layer that turns access and authority into usable answers.

“In 2026, GEO/AEO will be essential for visibility, requiring content to be highly structured, fact-based, and semantically rich to be considered authoritative enough for AI to reference.

Rather than chasing rankings alone, SEOs must now optimise for inclusion in zero-click, conversational responses — ensuring that brand-owned content is answer-ready and aligned with user intent at a granular level.”

— Rejoice Ojiaku, Senior Content Specialist at Wise

(Check out Rejoice’s Lumar webinar session: “SEO in the Age of AI: A Conversation on GEO, AEO & the Future of Search.”) 

How Content GEO influences AI search visibility

A strong Content GEO strategy can improve four key aspects of AI search visibility.

1. Extractability

Extractability is how easily an AI system can pull a clear, factual answer from your page.

Content that buries the answer under long intros, vague claims, or disconnected paragraphs is harder to reuse. Content that states the answer clearly, defines key terms, and explains the surrounding context is more likely to support AI-generated responses.

2. Citation preference

Citation preference is whether an AI system chooses your content instead of another source.

For competitive topics, AI systems often have multiple possible sources to draw from. Your content needs to give them a reason to prefer yours: clearer explanations, stronger evidence, better specificity, fresher information, original insights, or more complete coverage.

3. Topical association

Topical association is how consistently AI systems connect your brand with a specific subject area.

One optimized article rarely builds durable AI visibility on its own. Content GEO works best when individual pages support a broader topical footprint. Over time, your content should help AI systems understand what your brand is known for.

4. Completeness

Completeness is whether your content covers enough of the topic to satisfy the user’s real question and likely follow-up questions.

AI search experiences are conversational. A single prompt may imply several related needs. Content that only answers the narrowest version of a query may be less useful than content that anticipates the user’s next question, defines scope, addresses nuance, and points toward the right next step.

What Content GEO is not

Content GEO is not keyword stuffing for AI. It is not adding an FAQ block to every page and calling the job done. It is not generating thousands of generic articles to “feed” LLMs.

In fact, generic AI-written content can make the problem worse. If every brand publishes the same surface-level explanations, AI systems have little reason to choose one source over another.

Content GEO is about making your content more distinctive, more useful, more structured, and more grounded in real expertise.

That can include original research, expert commentary, unique customer insights, product knowledge, practical examples, data, comparisons, and clearly sourced claims. 

The goal is not simply to produce more content. The goal is to produce content that AI systems can confidently interpret as useful source material.

Your Content GEO checklist

The later posts in this GEO/AEO series will cover specific Content GEO tactics in depth. For now, use this as a strategic overview of what your GEO content program should be working toward: 

□  Write clear, factual, answer-first content that directly addresses user questions.

□  Optimize for semantic relevance and full coverage of related sub-topics.

□  Build evidence-based content with citations and sources.

□  Structure content into easily retrievable “chunks” (clear sections with H2/H3 headings.

□  Ensure entity clarity in ‘chunk’ passages (avoid pronouns replacing key entities).

□  Create multimodal content (video, audio, images) and add transcripts to video/audio; descriptive alt text to images.

□  Demonstrate E-E-A-T (Experience, Expertise, Authority, Trustworthiness) through expert authorship, original data, and transparent sourcing.

“The way forward is to treat content as a cross-platform ecosystem. Make videos, images, and audio AI-friendly with proper metadata, captions, and transcripts.

Ensure entity and topic alignment so AI systems recognize your authority, no matter the format.”

— Jon Clark, Managing Partner at Moving Traffic Media

• (Hear more from Jon in his Lumar GEO/AEO webinar session, “The New Metrics of AI Search – GEO/AEO KPIs You Should Track Now.” )

The bottom line: content is becoming AI source material

Content has always helped brands educate audiences, build trust, and drive demand. In the AI search era, it has another role: it becomes source material for the systems shaping how people discover, compare, and choose brands.

That does not mean every page should be written for machines first. It means content teams need to recognize that machines are now part of the audience. The strongest content will serve both: useful enough for humans to value, and clear enough for AI systems to understand, retrieve, and cite.

The brands that win in AI search will not be the ones that publish the most. They will be the ones that make their expertise easiest to understand, verify, and reuse.

About Lumar’s GEO / AEO explainer series

In this Lumar series, we’re exploring strategies for generative engine optimization (GEO), also known as answer engine optimization (AEO) — that is, how to boost your brand’s AI visibility and likelihood of earning mentions or citations from LLMs and AI-powered platforms like ChatGPT, Claude, Gemini, Perplexity, or Google’s AI Overviews and AI Mode.

Explore Lumar’s powerful platform features for GEO content optimization

• GEO Content Evaluation Tools
• AI Visibility Tracking Tools
• The Full Lumar GEO / AEO Platform

Get Lumar’s FULL guide to GEO / AEO

Ready to dive deeper into how to build a strong GEO / AEO strategy for AI search? Get our full, free “AI Search Optimization Playbook” now.

The post How to Optimize Your Content for AI Search Visibility appeared first on Lumar.