Suddenly, people who never cared much about content structure, review cycles, or metadata are speaking as though a chatbot will descend from the heavens and sort out the last fifteen years of documentation neglect.

That isn’t what the data says.

The 2026 State Of AI In Technical Documentation

The 2026 State of AI in Technical Documentation Report, based on responses from around 400 participants, most of them experienced technical communicators, paints a much more useful picture. AI is already part of our work. It is helping us do more work faster, but it’s not trusted enough to allow it to run wild. And the biggest obstacles to getting more value from AI in tech docs aren’t technological so much as organizational, structural, and painfully familiar.

Which is to say, the profession has not been rescued by a machine. Instead, it’s acquired a new assistant with a speed and truth problems.

Get The Report

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A customer disaster story with a very modern lesson for tech writers and documentation leaders

A few years ago, while consulting with a financial services software company, I got pulled into one of those situations that starts with “a customer is upset” and ends with leadership acting stunned that documentation quality might affect customer retention.

Duh! 😒

The company sold an automated mortgage approval system to lenders. This wasn’t some whimsical app that guessed your aura or matched your leadership style to a breakfast pastry (<— that’s not a thing, yet). It was serious software used in highly regulated environments, where customers needed clear, trustworthy guidance to deploy, configure, and use it correctly.

One customer had had enough. ✋🏼

They’d spent a long time (and wasted a lot of money) trying to make sense of the material the vendor had provided. And “provided” is generous at best.

What they’d actually received was less a set of docs and more a content yard sale assembled by well-meaning people who’d apparently never met a governance policy. There were formal docs, some of which may even have been accurate. There were PDFs from marketing, training materials, spreadsheets from internal teams, reference docs, bug notes, release notes, future roadmap tidbits, links to files marooned in SharePoint, and yes, emails.

All of it had been bundled into one giant zip file, as if customers enjoy panning for truth in a river of miscellaneous corporate debris.

They do not.

After several frustrating meetings, the customer used the cancellation clause in their contract and walked away. They also threatened a lawsuit for breach of contract. Then the financial services press got wind of it, and other lenders who’d bought the same software also started to rethink their choices. Leadership panicked, which was understandable.

Because I was there to help the company mature its doc practices, I got sent onsite to find out what had gone wrong and help stop the bleeding. 🩸

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Both are real. Both are useful. Both sound like they arrive at work on time. ⏰

Docs-as-a-Hot-Mess is different.

👉🏾 It isn’t a methodology.

👉🏾 It isn’t a best practice.

👉🏾 It isn’t a movement anyone should claim in public.

It’s simply a label for a condition many organizations already have: docs produced by multiple internal groups, for multiple purposes, with multiple assumptions, and with no governing framework strong enough to reconcile any of it.

The naming convention is really the only thing these categories have in common.

They all start with “Docs-as-,” which gives them a faint air of professionalism, much like putting a blazer on a bad decision. But where Docs-as-Code suggests rigor and Docs-as-Tests suggests verification, Docs-as-a-Hot-Mess describes what happens when nobody’s clearly modeled the logic underneath the content and then acts surprised when an AI answer engine turns that confusion into customer-facing prose.

That’s why we need the term.

A lot of documentation that looked “good enough” in the old world isn’t ready for AI retrieval in the new one. A help topic written for support engineers sits next to an onboarding guide written for customers. A release note we produced assumes context the reader doesn’t have. A troubleshooting article we crafted skips the event that starts the workflow. A procedure we carefully worded says “the user” does something, but never explains which user, in what role, under what conditions, or whether the system was supposed to handle it automatically instead.

That isn’t guidance. That’s improv with screenshots.

And AI answer engines, bless their tireless little hearts, are very good at finding disconnected scraps, flattening their differences, and serving them back as one smooth, confident answer. Which is great when your content is governed, structured, and semantically clear. If it isn’t, the machine doesn’t fix the mess. It scales it. Publicly.

AI Didn’t Create Our Mess, But Turned On The Lights So We Couldn’t Help But See It

A customer asks a simple question. The AI assistant replies in seconds with the confidence of a man explaining olive oil at a dinner party. It has found a setup topic from the help center, a support note meant for internal use, a few release-note fragments, and an onboarding PDF that appears to have been updated during a period of low morale. It blends them into one polished answer. The answer is clear, direct, wrong in at least three ways, and just plausible enough to cause trouble.

This is usually the part where someone says, “Well, the AI hallucinated.”

Sometimes it did. But often it’s doing something worse and far more understandable: it’s retrieving exactly what you gave it.

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That’s where the idea of a content integrity model becomes useful. It isn’t a formal standard with a grand governing body and a tasteful logo. It’s a practical way to think about whether the content feeding AI systems deserves to be trusted in the first place. Put plainly, it asks the questions technical writers should’ve been asking all along: where did this information come from, who verified it, is it still current, does it agree with related content, and can anybody trace how it got approved?

What the Content Integrity Model Actually Means

At heart, the content integrity model (as presented by Rahel Anne Bailie) is less a branded framework than a disciplined habit of mind. It treats content as something that must be trustworthy before it can be useful, especially now that machines are reading, summarizing, and repackaging it for other people. It pushes you to look at content not merely as words arranged into a page, but as an asset with a source, a history, an owner, and consequences.

That matters because AI systems are very good at sounding sure of themselves even when they’re spectacularly wrong. They don’t hesitate and they don’t blush. They don’t bother to stop and say, “This section was last updated five years ago, so perhaps let’s proceed with caution.” They just produce answers with the confidence of a man explaining the health benefits of a supplement he found in a podcast ad.

If our underlying content is weak, generative AI output won’t improve. It’ll just become a smoother, faster delivery mechanism for confusing information.

Why This Matters More Now Than It Did Before

Before AI entered the chat, our not-so-good content mostly stayed where it was. A stale help page remained stale. A contradictory procedure confused only the unlucky few who stumbled upon it. A vague warning sat quietly in the corner, radiating menace (but not doing much until someone tripped over it.) The damage was real, but it was often localized.

AI flips the script. Once machines begin reading our content and turning it into answers, summaries, or guided experiences, our weak source content doesn’t stay politely tucked away. It gets amplified. Contradictions surface faster and gaps become more, well…obvious. Outdated docs get handed to our users as though they just came down from a mountain on stone tablets.

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Content Strategy Is The Part No One Sees — Until It Breaks

Most people think content strategy is about planning what to write. That’s part of it, but it’s not the part that saves us when things go sideways.

Content strategy is the system behind the content — the decisions that determine:

• why something exists

• who it’s for

• how it’s structured

• where it shows up

• and who’s responsible for keeping it accurate over time

It’s what keeps content from becoming a scattered content hairball of “we should probably document that” moments.

It’s easy to spot the absence of content strategy pretty quickly. If our help center includes multiple articles that say almost the same thing, but not quite — chances are good, there’s no formal content strategy in place.

If our documentation includes a procedure that works until step four quietly assumes the user already know something no one bothered to explain to them, content strategy is lacking.

If our customers have to ask, “Which version is correct?” and the honest answer is… “it depends” — a content strategy is maybe, probably, definitely missing in action.

We don’t have a technical writing problem. We have a system problem.

For tech writers, this matters more now than in the past Our job isn’t just about producing clear instructions anymore. It’s about designing information so it holds up across systems, channels, and increasingly, AI-powered machines that read and reuse our content in ways we don’t control.

When content lacks structure and governance, it doesn’t just confuse readers — it confuses the systems trying to interpret it. AI doesn’t fix that. It amplifies it. If your documentation contradicts itself or buries important context, those issues don’t disappear when an AI assistant summarizes the content. They get repackaged into answers that sound confident whether they’re right or not. Research from the BBC has shown that generative AI can misrepresent source material even when it’s trying to summarize it.

That’s where content strategy earns its keep. It’s what makes content consistent enough to trust, structured enough to reuse, and governed enough to maintain. Without it, every new piece of content adds a little more entropy. With it, content starts to behave like infrastructure — something you can build on instead of constantly patch.

A sound content strategy also changes how your work is perceived by others. When there’s no strategy, writing can look like a task anyone could do if they had the time. Writing is just typing, right? How hard can it be?

When there is a strategy, it becomes clear that someone has to think about how information fits together, how it evolves, and how it supports both users and the business.

That someone is usually the tech writer — whether the job description says so or not.

So if content strategy sounds abstract, it’s really not. It’s the difference between content that sort of works today and content that keeps working tomorrow. And in a world where your documentation feeds everything from help portals to AI-generated answers, that difference isn’t academic. It’s operational. 🤠

That optimism isn’t wrong. It’s just usually buried under 47 tabs, three half-read white papers, and a YouTube recommendation engine that thinks “AI hype explainer” is what you needed instead of something you can actually use on Monday morning.

Which brings us to a quieter, more useful corner of the internet.

The Library You May Not Know You Needed

If you spend your days wrangling content — shaping it, fixing it, defending it from well-meaning chaos — you eventually realize something: you don’t just need ideas. You need tested thinking. You need to hear how other teams solved the same mess you’re currently staring at.

That’s what you’ll find on The Content Wrangler channel on BrightTALK isn’t a highlight reel. It’s an archive — and a substantial one.

More than 800 recorded sessions, most of them 60 minutes or so, covering everything from content operations and governance to AI, personalization, structured authoring, and the occasional existential crisis about whether anyone reads the docs.

And unlike the average “thought leadership” content that floats by on LinkedIn, these sessions tend to feature people who’ve actually done the work — and are willing to talk about what didn’t go smoothly along the way.

Subscribe (It’s Free) And Save Yourself The Hunt

You could keep searching for answers one blog post at a time, hoping the algorithm has a moment of clarity. Or you could subscribe (it’s free) and give yourself a reliable place to go when you need ideas that have already survived contact with reality.

Subscribing means you’ll get notified about upcoming sessions, but more importantly, it means you’ll stop starting from scratch every time a new challenge lands on your desk. And if you’ve been in this field long enough, you know how often that happens.

Subscribe. Explore. Steal the good ideas. Avoid the bad ones.

And the next time someone asks, “Has anyone figured this out yet?” you’ll be able to say, “Yes. And I know exactly where to find them.” 🤠

Virtual humans don’t understand our products. They don’t apply sound judgment. They don’t magically clean up our vague, inconsistent, or poorly governed docs. They simply turn our content into answers and deliver those answers with a face, a voice, and far more confidence than your source material may deserve.

That’s the real issue.

The interface may look polished, but the underlying system is still drawing from whatever content you gave it. If your docs are messy, contradictory, or imprecise, the virtual human won’t fix that. It’ll perform it.

For tech writers, that means our jobs are changing in important ways. We’re no longer writing only for people who read what we write. We’re increasingly writing for systems that retrieve, assemble, and speak on our behalf. And those systems don’t handle ambiguity with the grace of a seasoned support rep. They just answer and keep moving.

Watch the clip. Then ask yourself a blunt question: if your docs started talking today, would you trust what they’d say? 🤠

If you’re interested in learning about conversational interfaces, stop by the Emerse.ai website and explore how it works and check out their virtual training library.

There is a particular kind of meeting happening in offices everywhere right now, and it usually begins with someone from leadership saying something cheerful and faintly menacing like, “With AI, we should be able to create documentation much faster.”

This is the sort of sentence that sounds harmless until you realize it is often followed by a silence in which every tech writer in the room is expected to smile politely while their profession is reduced to “person who types words into boxes.”

The implication is hard to miss. If AI can generate sentences, and structured content can feed the machine, then surely the technical writer is now just an expensive hallway monitor for punctuation and product names.

Unfortunately for the people saying this, that is not how any of this works.

Yes, AI can generate language. Quite a lot of it, quickly. It can produce whole fields of prose with the confidence of a man at brunch explaining blockchain to people who did not ask. But language alone isn’t knowledge, structured content alone isn’t meaning, and neither one automatically produces an answer a human being can trust, understand, or use without accidentally setting something expensive on fire.

That is why three unfashionably academic little “-ologies” matter more than ever: ontology, epistemology, and phenomenology.

Yes, I know. It sounds like somebody spilled a philosophy department onto a docs team. Stay with me.

These are not decorative words for LinkedIn people who own too many blazers. These are concepts technical writers need to understand if they want to remain relevant in an AI-powered documentation world. More than that, they are the difference between being seen as someone who “writes help content” and someone who makes AI-delivered knowledge accurate, useful, and safe.

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The lineup features an impressive range of experts, including Sharon Burton, Rob Hanna, Ed Grzetich, Rahel Bailie, Sarah O’Keefe, Katie Williamson Sebkhi, Susan Kraft-Yorke, Anne Malinda, Justyna Hietala, Jason Kaufman, Torsten Machert, Wendy Lewis, Mark Pepper, and Louise Stengard Kristiansen.

Together, these professionals explore everything from AI-powered retrieval and messaging misalignment to traceable knowledge, documentation quality, semantic layers, structured content, localization strategy, and the hidden role metadata plays in helping people find and trust the information they need.

🗓️ [May 6] The History of the History of Women in Technical Communication

Hear untold stories that shaped technical communication as Sharon Burton, content strategist and editor, takes you behind the scenes of her new anthology, Women in Technical Communication (2026 XML Press). Discover how this groundbreaking collection came to life—and why documenting our history matters now more than ever.

Register

🗓️ [May 7] It’s The Content, *#%#*$!

Rob Hanna, CEO and Co-Founder of Precision Content, will dissect the critical role that source material plays in determining whether your AI delivers value or disappointment. Explore the real reasons behind “bad content in, bad content out” and discover practical steps you can take to ensure your AI-generated output meets expectations. You’ll gain actionable insights on how to identify weak points in your existing content pipeline and strategies for transforming flawed inputs into high-quality results.

Register

🗓️ [May 12] Why Documentation Format Matters More Than AI Model

Join Ed Grzetich, Technical Documentation Specialist, as he shares research from over 21,000 integration tests revealing a surprising insight: documentation format impacts AI-generated code quality more than which model you use. You’ll see why the wrong format can cause smaller models to fail completely, while optimized formats empower both advanced and basic AI tools to deliver better results.

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🗓️ [May 13] Conversational AI: The Cost of Ignoring Structured Content

Rahel Bailie, Content Solutions Strategist at Content Seriously, and host Sarah O’Keefe, Founder & CEO of Scriptorium Publishing, examine how the intersection of structured content and conversational AI has evolved. They also discuss practical next steps that organizations can take to create a successful AI content strategy.

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🗓️ [May 13] How to Get Buy-In, Show Value, and Earn a Seat at the Table

Discover practical strategies for influencing your team and stakeholders—even when you don’t have formal authority. Katie Williamson Sebkhi, Director of UX Content Strategy at Cloudflare, will share proven tactics to help you get buy-in, demonstrate value early, and secure a seat at the table without resorting to endless explanations.

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🗓️ [May 14] A $3M Penalty: The Hidden Cost of Bad Technical Documentation

In this session, Susan Kraft-Yorke, Information Architect at Kraft Consulting, reveals how inconsistent document practices can quietly erode client trust, slash revenue, and undermine team productivity—even costing companies millions in preventable penalties.

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🗓️ [May 14] Scientific Communication for Technical Writers

Anne Malinda, QA Document Control Specialist at Encoded Therapeutics, will guide you through the shared foundations that unite these fields—clarity, accuracy, structured messaging, and ethical responsibility. Discover where science communicators and technical writers overlap, and pinpoint what truly sets them apart: audience and content, not ability.

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🗓️ [May 19] Struggles, Sins, Survival Strategies: The Secret Life of Tech Writers

Join Justyna Hietala, Senior Designer in Technical Documentation at Beamex Oy Ab, for an honest exploration into the frustrations, hidden challenges, and psychological hurdles that come with creating documentation. Step behind the scenes to examine why so many documentation tasks feel invisible—and why coping behaviors often emerge in response to relentless deadlines, shifting reviews, complex tools, and localization demands. Hear candid “confessions” from the trenches and learn how recognizing what you can control, influence, or choose helps reduce frustration and restore focus.

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🗓️ [May 19] Destroy Enterprise Knowledge Chaos for Hallucination-Free GenAI

Join Jason Kaufman, CEO & Co-Founder of Zaon Labs, as he reveals why documentation — not just model tuning — holds the key to trustworthy generative AI.
Explore how incomplete, conflicting, or outdated technical content forces AI systems to fabricate answers, propagate obsolete workflows, and confuse end-users. Learn practical methods for identifying documentation gaps, harmonizing information across platforms like SharePoint and wikis, and establishing update cycles that keep your knowledge base current and reliable.

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🗓️ [May 20] How To Survive The Four Horsemen of the “AIpocalypse”

Torsten Machert (Congree Language Technologies) and Sarah O’Keefe (Scriptorium Publishing) reveal the four biggest threats undermining quality when you rely on GenAI for content creation, and explore actionable strategies to address these challenges so your output remains consistent, accurate, and unmistakably yours.

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🗓️ [May 21] The Semantic Layer: A Hidden Capability That Makes Data Useful

Join Wendy Lewis, Knowledge Engineer at UKG and datatomato, as she unpacks the role of the semantic (or knowledge) layer in transforming scattered info into actionable insights. Explore what a semantic layer is, why it matters, and how it works to align language, reduce ambiguity, and connect teams across your organization.

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🗓️ [May 21] Best Practices For Tech Writers Moving To Structured Content

Join Mark Pepper, Principal Consultant at Crimson Sage Softworks Incorporated, as he demonstrates how to conduct effective content audits that reveal what you have — and what you need. Discover how to design a taxonomy tailored to your business goals and map it directly to metadata, ensuring your content is organized, discoverable, and ready for reuse.

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🗓️ [May 28] Setting Up a Localization Strategy For Technical Documentation

Join Louise Stengard Kristiansen, Globalization Strategist at ProStrategy | House of Localization, as she reveals why so many localization efforts fail before they even begin and how you can avoid common pitfalls. You’ll learn how to assess your current localization maturity, design effective workflows, select the right translation management system (TMS), and implement governance frameworks that support quality and consistency.

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In his paper, “Programming a Computer for Playing Chess,” Shannon wasn’t trying to build a novelty act for brainy cocktail parties. He chose chess because it forced a machine to face the sort of problem machines are facing now: too many possible moves, not enough time to calculate everything, and no choice but to make a judgment anyway.

That should sound familiar to anyone watching generative AI answer questions about our products, policies, processes, or software configuration with the calm confidence of a man giving directions while standing in the wrong city.

“Tolerably Good” Was the Goal

One of the most useful things Shannon did was lower the temperature. He didn’t say machines had to play perfect chess. He said they had to play “tolerably good” chess.

He understood that perfect performance wasn’t realistic. The problem space was too large. The machines of the day couldn’t analyze every possible continuation. So the real challenge wasn’t perfection. It was usefulness.

Could the machine make choices that were good enough to hold up under normal conditions?

That’s still the question with AI now, even though people keep dressing it up in more expensive language. We don’t actually need AI to be magical. We need it to be useful without wandering off into fiction. We need it to stop sounding certain when it should be saying, “I’m not entirely sure, and frankly neither should you be.”

The trouble is that “good enough” means very different things to different people depending on the task. If AI drafts a mediocre summary of a meeting, nobody faints. If it gives our prospects or customers the wrong setup instruction, skips a prerequisite, or blends two different product versions into one smooth paragraph of nonsense, suddenly “tolerably good” starts looking like a phrase that should come with a lawyer.

Related: Generative AI Lawsuits Timeline (updated weekly)

The Machine Doesn’t Know — It Guesses, Confidently

Shannon understood something that people still resist because it ruins the fantasy. A machine doesn’t always arrive at the answer by knowing the answer. Often, it arrives there by evaluating possibilities and choosing what seems best according to the signals it has.

Modern AI works pretty much the same way. It doesn’t know your product the way an experienced support agent knows it. It doesn’t understand your docs the way a careful reader does. It doesn’t stop, scratch its chin, and ask whether two similar procedures might apply to different user roles. It predicts. It estimates. It assembles a response that looks like the kind of thing a good answer would look like.

When the signals are strong, this can feel impressive. When the signals are weak, missing, or inconsistent, it still produces something. That’s where the trouble starts. The output may sound measured and complete, which is often the first sign you should worry.

Coherence Isn’t the Same Thing as Accuracy

People often treat fluent, easy-to-process language as a cue that something is true. Psychologists call this processing fluency, and a large body of research ties it to truth judgments. In a classic study, statements that were simply easier to read were more likely to be judged true. The authors’ conclusion was blunt: “perceptual fluency affects judgments of truth.”

If the answer flows nicely, uses the correct nouns, and doesn’t visibly burst into flames, many assume the machine must have understood what it was talking about.

But Shannon pushes against that kind of wishful thinking. He knew the system would often rely on approximation. In chess, that means the machine makes a reasonable move without proving it’s the best move. In genAI, the machine doesn’t arrive at an answer by checking whether it’s true in the way a human expert might. It produces a response by predicting what a good answer is likely to sound like, one LLM token at a time. That’s why AI-generated output can read as polished, measured, and entirely sensible while still being wrong in ways that matter.

That distinction matters a lot for tech writers. A response can be coherent and still be useless. It can be elegant and still omit the thing that mattered most. It can sound like it belongs in the product manual while quietly steering the reader into a ditch.

That’s why the current conversation about AI so often feels silly. People keep asking whether the prose sounds natural. That’s the least interesting part. The dangerous part is that the prose sounds natural even when the reasoning underneath it is shaky enough to qualify as decorative.

The Real Issue Isn’t Intelligence — It’s Signal Quality

Shannon’s chess-playing computer needed a way to judge positions — basically, how the board was set up at that moment — when it couldn’t calculate everything. In other words, it needed signals that helped it decide what choices looked stronger, weaker, safer, riskier, more promising.

That should sound very familiar to tech writers working with structured content, metadata, taxonomy, ontology, versioning, or workflow status. Because in modern AI systems, those are the signals.

They tell the model, or the retrieval layer feeding the model, what content applies to which audience, which version is current, which procedure belongs to which product version, which warnings matter, and which info has been reviewed and approved by someone who’d prefer not to be sued.

Without those signals, the machine doesn’t throw up its hands and refuse to answer. That would be far too responsible. It keeps going. It infers. It blends. It smooths over cumbersome gaps. It performs the digital equivalent of a person nodding along in a conversation they stopped understanding eight minutes ago.

That’s why weak content governance keeps showing up as an AI problem. It isn’t always an AI problem. Quite often it’s a problem with our content that AI exposes.

Why Tech Writers Are Suddenly More Important

This is where the tech writer’s job gets more interesting, and perhaps a bit more annoying. 🤬 It used to be enough for us to write clearly for humans. Homo sapiens are patient in ways machines aren’t. We notice contradictions, sense when context is missing, and when it is, we infer from surrounding material. We pause when something feels off.

AI doesn’t do that. Nope. It doesn’t slow down because a sentence feels off. It doesn’t ask whether the warning in one document conflicts with caution advice in another. It doesn’t notice that the troubleshooting article applies only to Version 11 while the setup instructions were written for Version 9 and last updated when everyone still thought QR codes were futuristic.

So now our job is larger. We have to make context more explicit, surface conditions, boundaries, prerequisites, exceptions, and applicability in our content. And, we have to help the system distinguish between general guidance and situation-specific instruction.

It’s not glamorous, but it is important. The machine can only work with what the content provides.

We’re Not Dealing With A New Problem

The reason Shannon’s work still matters some 75+ years later is that he described the shape of the problem long before anyone had a chatbot to blame. A machine facing a large decision space can’t calculate everything. It has to use approximations (see video below to learn more). The quality of its behavior depends on the quality of the signals guiding those approximations. That was true in 1950. It’s true now.

What changed is that the machine no longer sits quietly in the corner playing chess. Now it drafts documents, answers support questions, summarizes policy, and speaks to customers in complete sentences (increasingly through human-like interfaces called digital avatars or virtual humans). That makes the underlying problem more visible, but it doesn’t make it new.

We keep pretending we’ve stumbled into some unprecedented crisis of machine unreliability. In truth, we’ve mostly put a friendly conversational interface on top of an old computational reality and then acted stunned when it behaved like a machine making approximations under constraint.

In fact, it may be useful for us to stop saying that AI systems hallucinate when they actually approximate.

Claude Shannon would not be surprised. He might be amused, though. 😆

We Should Stop Pretending We Don’t Know

If there’s a lesson here for us, it’s not that AI is broken or that machines are foolish or that prompts are the secret to salvation. It’s that reliability has always depended on bounded problems, explicit signals, and sensible expectations.

AI doesn’t need to be brilliant. It needs to be trustworthy within clear limits. That means the content behind it — our content — has to do more than read well. It has to signal what applies, what doesn’t, what changed, what matters, and what should never be blended into a paragraph just because the model found it statistically convenient.

Shannon knew that a machine making decisions under uncertainty would only ever be as good as the method guiding its choices. We’re still learning that lesson now, only with better branding and much more overconfidence. 🤠

Once you’re ready to learn more about putting Claude to work for you, consider taking this free online course, Claude 101.

Their online courses walk through the fundamentals with clarity. They also include advice on working with large language models (LLMs). If you follow their guidance, your writing will likely improve. Your docs should become easier to read; your intent harder to misinterpret.

It feels like progress. And it is. But only up to a point.

The Subtle Shift No One Can Ignore

Tech docs have a new reader. Not a person skimming for answers while jumping between online meetings (and while distracted by a steady stream of Slack notifications) but a system that retrieves, assembles, and delivers those answers on demand.

The system does not read the way humans do. Instead, it predicts, reconstructs, and responds. It takes what you wrote and turns it into something else.

Google’s guidance helps us with that process. Clear sentences and logical and consistent content organization give the model something to work with. But clarity alone does not determine what the model produces.

That is where the gap begins.

When The Model Fills In The Blanks

Large language models are good at producing language that sounds correct. They’re less reliable when the source material is incomplete or inconsistent.

Unlike humans, when context is missing, the model does not pause and ask us for clarification. Instead, it proceeds full steam ahead.

Researchers at Stanford University have shown that models can generate citations that look real but do not exist. The titles and authors seem credible at first glance. The problem is the sources are fabricated, and far too often, completely detached from reality.

Other research shows that LLMs can explain what code does while missing why it was designed that way, because the reasons why weren’t captured in the docs to begin with.

Reporting from The New York Times has identified cases where AI assistants combine outdated and current info into a single answer without signaling the conflict.

These aren’t edge cases. They’re predictable outcomes. The model isn’t inventing problems. It’s exposing the gaps.

What Clear Writing Can And Cannot Do

I think Google’s guidance on clear sentences and organizing content is fairly solid.

• Clarity reduces ambiguity ✅

• Structure improves comprehension ✅

We know these things are required to produce usable docs. But, they’re not enough if we’re hoping they’ll help us obtain reliable AI output.

A polished sentence may still lack context. A neatly structured page may still leave out key constraints. And a clear explanation may apply only to one version, audience, or configuration without ever saying so.

LLMs do not know what we meant to include, but didn’t. It only works with what we provide it.

The Context That Never Made It Into The Content

This is where Google’s guidance starts to show its limits. The courses offered focus on how to write. They do not address how to encode meaning in a way machines can reliably interpret.

Consider metadata

Google tells us to define our audience, but not to encode that audience in a way that our systems can recognize and act upon.

Without metadata, important differences between user roles, product versions, and situations stay hidden. The model may blend them together into one answer that sounds correct but doesn’t really fit any of them.

Consider taxonomy

Google encourages clear headings (a solid suggestion) but does not address controlled vocabulary or semantic consistency.

People usually know when different words are just different outfits on the same idea. Dog, pooch, hound, pup, mutt — no one needs a crisis counselor to sort that out. Context does the work.

LLMs do not always manage that trick gracefully. Sometimes they treat minor wording changes as if the universe has shifted on its axis. Other times they blur important differences and act as though related terms all belong in one big semantic junk drawer.

Consider provenance

Google says to review AI output, but it doesn’t explain how to check where the source content came from, whether it is still valid, or whether anyone approved it. Without that information, old or unverified content can show up next to current guidance with no warning that it may be risky.

Consider content models

Google discusses organization at the document level, but not structure at the semantic level. A content model defines what types of content exist, what components they contain, and how those components relate. Without it, content remains a collection of pages. With it, content becomes a system that supports consistent interpretation.

A Familiar Scenario To Illustrate This Challenge

Imagine a doc set that includes a setup guide that someone wrote two years ago, a troubleshooting article you updated yesterday, and a release note explaining that the old instructions no longer work after a recent update.

Individually, each doc is clear and well written. None of them share consistent metadata, terminology, or version context.

Your user asks an AI assistant how to set up a feature. The assistant pulls info from all three documents and combines it into an answer that sounds clear and complete. But the answer is wrong for that user’s version, setup, or situation.

The problem isn’t that one document was badly written. The problem is that the content didn’t include the structure, labels, and context the AI needed to tell what applied and what didn’t.

Real-world example: [It] kept telling me that that I had to use this one envar to put the app in debug mode. But it wasn't working at all. So eventually I get around to looking deeper into it and it turns out that this method was used 2 versions prior to the current version. — GITHub post Very outdated answers and wrong suggestions everywhere

What Google Teaches Us — And What It Leaves for Us To Learn On Our Own

Google’s courses establish a baseline. They teach writers to produce content that is clear, structured, and easier to process. That baseline matters. Without it, nothing else works. Period.

But it fails to address the factors that determine whether AI-generated answers are accurate, consistent, and trustworthy. Those factors include structure beyond formatting, context beyond prose, and governance beyond editorial review.

They include decisions about how meaning is represented, how relationships are defined, and how trust is established. That work is not covered in the courses.

The Work That Now Defines Our Role

We tech writers have always been responsible for clarity. Now we’re also responsible for context. That includes making intent explicit, encoding meaning in ways machines can interpret, and ensuring that our content can be trusted when it’s retrieved, combined, and delivered outside its original form.

This isn’t a shift away from writing. It’s an expansion of what our writing must accomplish.

The sentence still matters. But the system that surrounds it matters more.

The Difference Between Readable Content And Trustworthy Content

Google will help you write docs that AI can read. It won’t help you create docs that AI can trust. And as AI takes on a larger role in delivering answers, that distinction moves from academic to operational. Those of us that recognize this distinction will design content accordingly. The rest of us will continue to wonder why their docs sound right but behave unpredictably. 🤠

▶️ Watch the entire interview (60 minutes, on-demand).

👉🏾 Users still need it.

👉🏾 Support teams still depend on it. A

👉🏾 AI systems are increasingly consuming it as source material

The need hasn’t disappeared. What has changed is the environment in which documentation gets created, maintained, delivered, and now interpreted by machines. That’s the central point Ondrej Tesar (documentation lead at Pricefx) makes in a recent LinkedIn post, and it’s a useful one.

Tesar argues that AI-assisted coding is accelerating software development while doc teams risk falling behind. He points to a structural asymmetry: software teams often benefit from cleaner, more machine-friendly inputs, while documentation work still depends heavily on context gathering, judgment, and knowledge that may not be fully captured in formal systems. He also argues that the hardest part of the work lives in the undocumented remainder: tribal knowledge, edge cases, and design rationale that do not show up neatly in publicly available product information.

That argument is directionally right, but it needs one important correction.

Not All Doc Teams Are Working From A Swamp Of Vague Jira Tickets

Not all documentation teams are working from a swamp of vague Jira tickets, random Slack messages, and whatever an engineer muttered before running to another meeting. Some teams have spent years building semantically structured, metadata-rich content operations using XML, DITA, controlled vocabularies, reuse models, governed workflows, and component content management systems.

Those teams aren’t merely “writing docs.” They’re building machine-processable knowledge assets. And in a world increasingly shaped by retrieval, summarization, answer engines, agents, and large language models, that matters a great deal.

This distinction isn’t trivial. It is the whole ballgame.

The Capability Multiplier: Semantic Structured Content

When docs are semantically structured, they become easier to retrieve, reuse, validate, govern, securely personalize, and transform. It becomes easier for both humans (and those pesky machines upon which we rely) to determine what a chunk of content is, how it relates to other chunks, when it applies, what conditions constrain it, and whether it belongs in a given output.

Structured content does not magically solve every documentation problem, but it gives us a far stronger foundation for solving them. That’s been true for years. AI just made the consequences easier for us to see.

In other words, the problem is not “documentation is inherently messy.” The problem is that too many organizations still produce documentation in immature ways, then act surprised when those methods do not scale under modern delivery pressure.

This is where Tesar’s post becomes especially useful for tech writers. He notes that writing itself is only a fraction of the job. The larger share of labor involves gathering information, reconciling contradictions, understanding audience needs, interpreting product behavior, identifying what matters, and spotting what was omitted. He cites Tom Johnson’s framing that perhaps 20 percent of the work is writing and the rest is everything surrounding it. Whether or not we agrees with the exact percentage, the broader point stands: technical communication is not a typing contest.

And this is precisely why “AI can write now” is such a flimsy managerial fantasy.

Yes siree, AI can generate paragraphs. So can Craig from marketing, and no one is proposing we let him document authentication flows unsupervised.

The hard part was never producing sentences. The hard part is producing trustworthy knowledge. That means someone must still determine what is true, what changed, what applies to which audience, what assumptions need to be made explicit, and what warnings absolutely can’t be buried three clicks deep under a cheerful heading called “Additional Notes.”

Tesar is also right to point out the validation gap. Code usually moves through a layered quality process that may include peer review, automated checks, security scans, tests, integration validation, and QA.

Documentation often gets a peer review if the stars align and someone remembers it exists before release day. Tesar’s point is not that doc teams are careless. It’s that the validation pipeline for documentation is generally less automated, less consistent, and more dependent on human judgment than the validation pipeline for code.

Again, structured content changes this equation.

Structure Makes Better QA Possible

The more structured and governed our content ecosystem is, the more we can test for consistency, completeness, reuse integrity, metadata accuracy, conditional logic, terminology compliance, and output behavior. We may not get the exact equivalent of a unit test for every paragraph, but we can move far beyond “well, Cheryl read it and said it looked fine.”

Then There’s This: AI Consuming Documentation Directly

Tesar argues that writers now need to write not only for human readers, but also for AI systems that index, retrieve, and assemble answers from docs. He recommends self-contained sections, explicit context, and semantic structure so that AI systems can interpret content correctly when surfacing it through chatbots and agents.

This is where the old “good enough” publishing mindset starts to smell like salmon left in the office microwave over the weekend. It’s not that salmon is inherently offensive. It’s that under the wrong conditions, neglected too long, it becomes a biohazard.

Unstructured or weakly governed documentation behaves the same way in AI systems.

A human reader may be able to compensate for vague headings, missing prerequisites, buried conditions, inconsistent terminology, and context scattered across six pages and a prayer. A retrieval system or LLM may maybe, probably, certainly won’t. It may reuse the wrong chunk, miss the condition that changes everything, smooth over ambiguity, and return an answer that sounds polished while being 95% wrong. That’s not an edge case; it’s a design problem.

Tesar also raises a related concern: when LLM-generated content becomes source material for other LLMs, factual quality can degrade even if the language remains fluent. He cites research and commentary on how this sort of recursive training leads to model collapse, along with a study suggesting that models can explain what code does while missing why certain design decisions were made unless design documentation is provided as context.

His point is that rationale, judgment, and context don’t reliably survive abstraction unless someone captures them intentionally.

Tech writers should take that very personally. Because if docs becomes the primary knowledge layer consumed by AI systems, then structure, semantics, provenance, and governance are no longer back-office concerns. They become part of the user experience and shape whether the machine gives a useful answer, a partial answer, or a dangerously confident hallucination dressed in impeccable grammar.

That has implications for us and our managers alike.

For writers, the lesson is not “resist AI.” It is “stop acting as though sentence production is your most defensible skill.”

Let AI help with drafting, restructuring, summarizing, pattern-based content generation, and other repetitive tasks where it can provide real leverage. Tesar makes this case directly. But do not confuse that leverage with replacement. The durable value of technical communicators lies in judgment, structure, audience awareness, quality control, and the ability to turn scattered product reality into usable knowledge.

For managers, the lesson is even simpler: do not cut the documentation team because text generators exist.

Tesar explicitly argues against that move and warns that faster development without corresponding documentation capability will widen the backlog and amplify content debt. If development accelerates while docs remain under-resourced, under-tooled, and structurally immature, the result is not efficiency. It is faster delivery of confusion.

And if our docs are feeding AI-driven support, self-service, onboarding, and product guidance, that confusion will not stay politely hidden in our help center. It will be delivered directly to our users at scale.

So, Tesar is right: documentation is not dying. But the way many organizations create it is overdue for an intervention.

The future does not belong to teams that merely publish more words faster. It belongs to teams that create content machines can interpret, humans can trust, and organizations can govern. It belongs to teams that understand semantic structure is not publishing overhead. Instead, semantic structure is operational leverage. It supports interoperability, traceability, and portability. And, it makes systematic content reuse possible.

That’s been true for a long time. The difference now is that AI has made it much harder for everyone else to ignore. 🤠

That may sound less exciting than a keynote about AI reshaping civilization before lunch. But it has the distinct advantage of being true.

A great deal of productivity improvement can be achieved by removing friction from our work itself. Not from adding a robot on top of a mess and hoping — 🤞 — for the best. And, not from asking a language model to draft accurate prose while the rest of the workflow remains an obstacle course.

The good news is that tech writers have plenty of non-AI ways to work faster, more accurately, and with a lot less daily annoyance.

Start With The Repetition That Is Draining Your Soul

Take text expanders. They aren’t glamorous. Not at all! Nobody is going to introduce them onstage while dance music throbs and somebody from marketing uses the phrase “transformational workflow acceleration.” But they work.

If you regularly type the same product names, disclaimers, support instructions, links, trademark language, or stock email replies, a text expander can save time and reduce errors. Instead of retyping boilerplate from memory and making a fresh mistake because your brain checked out at 3:40 p.m., you type a shortcut and approved text appears instantly.

That’s not flashy; it’s helpful.

And text expanders point to a larger truth: if you keep writing the same things by hand, you probably don’t have a writing problem. You have a reuse problem.

Reuse Is Not Laziness. It Is Maturity.

Tech writers typically spend far too much time recreating material that should already exist as a reusable asset:

• Prerequisites

• Warnings

• Note language

• Standard procedures

• Product descriptions

• Related-links blurbs

• Release-note intros

If we rebuild that material from scratch every time, we’re wasting time and creating inconsistency at the same time, which is an impressively bad bargain.

It doesn’t matter much whether our tech docs team manages reuse through snippets, templates, shared files, a help authoring tool, or a component content management system, especially if you’re just getting started and your content maturity isn’t as mature as you’d like.

What matters is that repeated content gets treated as repeated content, not as a fresh creative-writing exercise every Thursday afternoon.

The fastest paragraph to write is the one you do not have to rewrite.

Related reading: Fundamental Concepts of Reuse (free, downloadable PDF chapter from the book, Managing Enterprise Content: A Unified Content Strategy by Ann Rockley, Charles Cooper)

Blank Pages Are the Enemy of Efficiency

Templates are another productivity tool that doesn’t get nearly enough love, possibly because they don’t sound innovative enough to attract a venture capitalist. 😆

But seriously, if your team routinely creates release notes, installation guides, API reference pages, migration instructions, troubleshooting topics, or knowledge base articles, nobody should be starting from a blank page every time like a settler arriving in an uncharted wilderness.

Templates likely reduce decision fatigue by eliminating unnecessary choices, lowering cognitive load, and acting as memory aids. While direct studies on content-creation templates are limited, adjacent research on choice overload, cognitive load, and checklists supports the claim.

That said, well-designed templates help writers move faster because the structure is already there. They also improve quality because they make it harder to forget the important stuff. If a good template includes fields for prerequisites, expected outcomes, permissions, dependencies, rollback instructions, or related links, we’re less likely to publish content that leaves our user stranded halfway through a task with the emotional support of a vague sentence and a screenshot from last year’s planned roll-out that never occurred.

Templates do not just save time. They help prevent rework.

A Style Guide Should Prevent Fights, Not Start New Ones

A practical style guide is another non-AI productivity booster hiding in plain sight.

Not the kind of guide that reads like it was written by an offended grammarian in a candlelit tower. The useful kind. The kind that answers real questions writers have every day. Do we bold user interface labels? How do we punctuate headings? What tone do we use in troubleshooting content? Do we write in second person? How do we refer to product features consistently?

When writers do not have to stop and guess, they move faster. When editors do not have to make the same correction over and over, they move faster too. When reviewers are not inventing their own editorial standards from the depths of their personal belief system, everybody suffers less.

A good style guide is not bureaucracy. It is a ceasefire agreement.

Video ▶️ Melanie Denise Davis (The AI Wrangler) interviews AI training specialist, Bill Raymond, about how AI can extract a style guide from existing docs and help refine, enforce, and reveal content gaps to address next

Terminology Chaos Is a Productivity Problem

One of the easiest ways to slow down a documentation team is to let terminology drift all over the place like a shopping cart in a windstorm.

If engineering calls it a workspace, marketing calls it a hub, support calls it an environment, and the documentation calls it a project, the user is going to assume the company is held together with staples and regret. Writers lose time every time they pause to wonder which term is correct. Editors lose time fixing inconsistent language. Reviewers lose time arguing about words that should have been settled long ago.

A shared terminology list helps writers draft faster, edit faster, and publish with fewer corrections. It also improves findability and makes content easier for users to understand.

Nope, it’s not thrilling. Yep, it matters a lot.

Read more

That’s why the 2026 State of AI in Technical Documentation report is worth a look.

The report offers a useful overview of the findings, including how technical writers are using AI currently, which tasks they support with it most often, and where adoption is still getting hung up on entirely reasonable concerns like trust, governance, and integration.

The findings are based on responses from around 400 technical documentation pros, and nearly half of those respondents have more than 21 years of experience in the field. In other words, this is not a pile of hot takes from people who discovered documentation last Thursday.

The overview makes one thing clear: AI use is no longer theoretical for technical writers. A large majority of respondents report using AI either regularly or occasionally, with current use concentrated around editing, drafting, rewriting, and summarizing content. At the same time, interest is growing in more operational uses, such as metadata tagging, content classification, terminology work, and content reuse. That gap between what teams are doing now and what they want to do next is where the interesting story lives.

Which is why the full report matters.

The summary gives you the headlines. The full report gives you the texture, the context, and the evidence you’ll want when somebody at work says, “So what should our documentation team be doing with AI?” It’s much nicer to answer that question with research than with the professional equivalent of shrugging in business casual.

If you work in tech docs and you’re trying to separate signal from nonsense, request the free report.

Epistemology is the study of knowledge: how we know what we know, what counts as justified belief, what makes something trustworthy, and how we tell the difference between facts and the things that Chad from Product wrote on Slack three months ago with tremendous confidence and no supporting evidence.

Technical Writers Do This Already

For those of us who create product documentation for a living, epistemology lives at the center of our job, whether anybody uses the word or not. Every time you ask which source is authoritative, question whether a screenshot is current, push back on conflicting SME feedback, or wonder why the UI says one thing while engineering insists it says another, you are doing epistemology.

You are trying to determine what deserves to be believed (and what doesn’t).

That may sound suspiciously philosophical for a profession that also involves chasing version numbers and begging subject matters experts to review drafts, but philosophy sneaks into the work more often than most people realize. Documentation is full of judgment calls about evidence, trust, and authority.

The job isn’t (and has never been) only about writing clearly. It’s about deciding what is solid enough to write down in the first place.

Why It Matters More In An AI World

In an AI-powered documentation environment, epistemology is not optional. It is the difference between a knowledge system and a hallucination vending machine.

AI can generate answers that sound polished, plausible, and entirely divorced from reality. It doesn’t wake up in a cold sweat worrying about source content integrity. Nor does it stop to pause and think, “Perhaps I should verify whether this workflow changed in the last release.” It predicts likely language. That is its special skill.

Truth is yours.

So when we establish review processes, define source-of-truth systems, track provenance, maintain governance, and decide what content should feed an AI assistant, we’re doing more than content management. We’re building the conditions under which knowledge can be trusted.

What Epistemology Looks Like in Documentation

This isn’t just an airy concept for people who own elbow patches and enjoy saying “framework” before lunch. In documentation, epistemology shows up in plain, daily work.

It appears when we ask who on our team approved a procedure and whether that approval still stands. It appears when we compare what engineering says with what support says and realize they’re not the same things. It appears when we flag outdated content, challenge assumptions, verify a workflow in the product itself, or insist that a chatbot should not draw from unreviewed draft material just because it happens to be sitting in a repository looking 👀 available.

Epistemology is the discipline behind the question: why should anyone trust this answer?

Production and Delivery Both Live or Die by It

Epistemology matters to both documentation production and delivery.

Production is where wew decide what gets in, what stays out, who signs off, what evidence supports the claims, and how contradictions get resolved. Delivery is where those decisions either hold up beautifully or collapse in public like a folding chair at a church picnic.

A chatbot, search engine, help portal, or embedded assistant can only be as trustworthy as the knowledge discipline behind it. The delivery experience may look sleek and appealing, but if the content feeding it is outdated, unverified, contradictory, or based on corporate folklore, the user loses. And usually in a hurry.

The Real Job Beneath the Writing

Tech docs aren’t there to look organized in a repository. They’re there to help somebody do something correctly. Install the thing. Fix the problem. Configure the setting. Avoid the outage. Survive the compliance audit without developing a facial twitch.

None of that works well unless our information is accurate, current, and defensible.

Put bluntly, epistemology is how tech writers keep “helpful” from becoming “dangerously approximate.”

Why This Makes Tech Writers Harder to Replace

So the next time somebody implies that we’re just polishing sentences while the machines handle the smart stuff, feel free to smile in that weary, spiritually exfoliated way tech writers do.

Then explain that somebody still has to determine what counts as knowledge, which sources are authoritative, which answers are safe to deliver, and how the system knows the difference between official guidance and random corporate folklore.

That somebody is you!

And that is epistemology. 🤠

“This AI workshop nailed the sweet spot between basics and hands-on practice. What made it really work? The instructor adjusted on the fly based on where we were. Every session delivered something immediately useful—which, let’s be honest, is rare.”
— Kat Reierson, Manager, Product B2B Documentation at Docusign

Let’s be honest. A lot of tech writers are already “using AI,” which often means poking at ChatGPT between meetings, getting a paragraph that is 70 percent useful, 20 percent suspicious, and 10 percent strangely eager to invent things that never happened. That is not a workflow. That is flirting with automation in public.

Bill Raymond’s AI Workshop for Tech Writers and Content Creators is a better alternative: a live, seven-session online course built to help content professionals use AI in ways that are practical, repeatable, and less likely to end in regret.

It is offered in partnership with The Content Wrangler, and the next workshop runs on Tuesdays from April 28 through June 9, 2026, and costs $1,749 before discount.

Additional 2026 class dates:

• Summer — Tuesdays July 14 — August 25

• Fall — Tuesdays September 15 - October 27

• Winter — Tuesdays November 3 - December 22

💰 You can save 10% off the cost of registration when you use discount code — TCW — at checkout.

Register

Don’t Let This Opportunity Pass You By

What makes this course worth a second look is that it’s aimed at people who do actual content work, not people who enjoy saying the word “disruption” as though it were a sacrament. Raymond says their workshop is for technical communication and documentation specialists, content strategists, and knowledge teams trying to modernize workflows with AI.

The stated outcomes are practical:

• automate repetitive production tasks

• tailor AI output to fit voice and terminology

• and connect content choices to user journeys, measurable outcomes, and ROI

In other words, less behold the future and more please help me get this draft, audit, or rewrite done without sacrificing my dignity.

What You’ll Learn 📖

👉🏼 Week 1 covers AI foundations, ethical guardrails, and prompt basics.

👉🏼 Week 2 moves into personas and audience journeys.

👉🏼 Week 3 focuses on personalization, controlled prompts, and custom GPT instructions.

👉🏼 Week 4 tackles workflow bottlenecks and content ROI.

👉🏼 Week 5 looks at gap analysis, research angles, and drafting.

👉🏼 Week 6 shifts to prioritization using customer insight and advanced prompting.

👉🏼 Week 7 ends with final project presentations and a roadmap for ongoing AI-enabled content operations.

This workshop promises persona-driven workflows, ready-to-use prompt libraries, custom GPT instructions, evaluation checklists, and a documented project plan for AI-assisted content production.

Participants also complete hands-on exercises and receive a digital certificate of completion after presenting their final project. For tech writers, that matters. If you’re going to spend money on training, it should produce something sturdier than inspiration and a notebook full of underlined buzzwords.

About The Instructor

Bill Raymond is the founder and lead instructor at Cambermast and says he helps teams put AI to work quickly and responsibly, with training that is hands-on, practical, and focused on immediate next steps. He’s worked with organizations such as Microsoft, NASA, DocuSign, Duke Energy, SLB, Moen, Digital.ai, and Red Hat.

“Bill made it easy to show up and learn. He tailored each session as we went, kept things grounded and practical, and somehow made AI feel way less intimidating. I left with skills I could use right now—and a head full of ideas.”
— Kat Reierson, Manager, Product B2B Documentation at Docusign

The Underdog of Learning Resources

The 2025 Developer Survey paints an interesting picture. You’ve got your standard array of learning tools — Stack Overflow, blogs, online courses, and of course, videos. But lurking quietly in the background, capturing the attention of a staggering 68% of respondents, is technical documentation. It’s the often underappreciated rock star in a world obsessed with quick fixes and instant gratification.

Let’s break it down. Of the 33,454 developers who were polled (76% of them being the true “professional” variety and an additional 15% falling into the aspirational or adjacent developer category) there was a strong consensus. You need technical documentation to learn how to code. Let that sink in.

It’s clear we tech writers are holding the reins of knowledge, quietly helping software developers avoid the chaos of poorly understood libraries and buggy frameworks.

The Developer’s Best Friend: Technical Documentation

Now, we all know that developers have a way of loving their code more than they love life itself. 😆 Still, these same developers (mostly aged between 25 and 44) are relying on our docs to make sense of their tools, libraries, and frameworks. There’s a good reason why a significant 68% of survey respondents preferred it over other resources. It’s simple. It works.

While Stack Overflow answers might solve an immediate issue, they don’t explain why things work the way they do (and even when they do, there’s no guarantee it'‘s accurate). Enter technical documentation (the unsung hero of the coding world). Developers, especially those with less than 10 years under their belts, might not be able to remember every edge case for every function call, but the doc we produce help them get it when it matters most.

It’s the difference between frantically Googling “why does my program crash every time I try to run it?” and confidently consulting well-organized reference material that tells you what you’re doing wrong and how to fix it. It’s like the difference between using a map and wandering aimlessly.

Why Documentation Is the Bedrock of Success

Okay, I’ll admit that documentation isn’t exactly the glamorous, party-loving cousin of a slick YouTube tutorial or a rapid-fire Stack Overflow answer, but it is where foundational knowledge lives. For most developers, especially those in the 35% who have been coding for fewer than 10 years, learning to code with solid documentation means building a robust, scalable understanding that no quick fix or trendy blog post can replace.

The Case for Appreciating Technical Writers

So, dear tech writers, it’s time we stand tall and claim our rightful spot at the table of essential resources. Developers might use other tools for a quick fix, but when it’s time to dive deep into understanding and mastering a framework, they’re coming to us. Documentation isn’t just a reference; it’s the bedrock of their continuing success.

And to all the developers reading this: Next time you’re diving into a technical topic, remember that the person behind those docs aren’t just throwing a bunch of words into a file. They’re saving your proverbial butt one detailed, well-organized, thoroughly tested section at a time. And in a world where most things come with a “don’t ask, just Google it” disclaimer, isn’t that worth a little appreciation? 🤠

People say it with the calm confidence of someone handing you a brochure at a hotel check-in desk, as though the matter has now been settled. But the minute you try to apply that definition to real work, it falls apart. It doesn’t tell you why metadata matters, what it does, or why so many systems turn feral when metadata is missing.

It just sits there, sounding official.

For tech writers, that definition is too lazy to earn its keep. Yes, metadata is, technically, information about other information. Fine. So is a Post-it note that says “important,” and nobody’s building a content strategy around that.

What metadata actually does is far more useful. Metadata tells systems and people how to understand, sort, trust, filter, reuse, and deliver content. It gives content context. It makes important distinctions visible and helps us determine what a piece of information means, who it applies to, when it should appear, and whether anyone should rely on it in the first place.

Metadata Examples

A title is metadata. So is version number, audience label, status (e.g., draft, approved, or obsolete), security classifications, region tags, content types, languages, and indications that something is a warning (or caution or danger — all slightly differnt things) rather than suggestions.

In other words, metadata doesn’t just describe content from the outside like a museum placard beside a painting. It helps content function. It tells the machinery what this thing is and what should happen next.

That matters even more now because machines are increasingly part of the audience. Search engines, recommendation systems, chatbots, and AI-powered answer tools don’t read with human intuition. They don’t squint at a paragraph and think, “Ah, this seems to be meant for administrators using version 4.2 in regulated environments.” If that info isn’t explicit, the machine may not treat it as real. And then everyone gets a beautifully worded answer assembled from the wrong parts, which is always exciting in the least fun way possible.

This is why the old definition feels so flimsy to me. It makes metadata sound optional, academic, even decorative. In practice, metadata is operational. It’s the layer that helps content behave properly. Without it, our documentation may exist, but it won’t work nearly as well as it should or could. It becomes harder to find, more challenging to govern, nearly impossible to personalize, challengng to reuse effectively, and much far easier to misuse.

So no, metadata is not just “data about data.” Metadata is the information that makes content usable in the systems upon which we rely to do our work. It’s how we make context explicit — which is how we help people and machines know what they’re looking at and what they should do with it.

Yes, that’s a much less catchy definition, I realize. But unlike the old one, it might actually be worth something. 🤠