The AI Workshop for Content Creators, led by Bill Raymond, in partnership with The Content Wrangler, is designed to help close that gap.

This seven-week, live, instructor-led online workshop gives tech writers (and other content pros) a structured way to apply AI across research, planning, drafting, editing, and quality review. You won’t spend seven weeks watching demo videos masquerading as training. Instead, you’ll build prompt libraries, custom GPT instructions, evaluation checklists, and a plan for putting AI-assisted content production into practice. And Bill will be there to help you along the way.

Learn More and Register

The curriculum (see the week-by-week workshop breakdown at the end of this post) addresses problems tech doc teams already face:

• identifying content gaps

• understanding audience needs along their journey

• maintaining a consistent voice across doc sets

• accelerating content inventories and audits

• prioritizing improvements, and

• establishing editorial controls

The workshop also covers ethical guardrails and governance expectations, subjects that deserve more attention as AI becomes part of routine documentation production.

Each student develops a real-world individual or team project throughout the workshop. By the final session, you should have a repeatable workflow you can continue using and improving.

The workshop runs Tuesdays from September 15 through October 27, 2026, from 10 a.m. to noon Pacific time. Sessions are live and online, and participants receive a certificate of completion.

If you’re ready to move from occasional AI experimentation to a working system for producing and improving content, review the workshop details and register.

Curriculum

Learn More and Register

Inference load (noun)

The amount of unstated information a person or machine must supply to understand content, connect related facts, or decide what to do.

About Inference Load

What Creates Inference Load?

Inference load is created when technical documentation leaves important relationships implicit. Readers and AI systems must determine how facts fit together, whether a rule applies, and what conclusion the content supports. Neither people or machines are particularly good at that.

What Inference Load Looks Like

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Because I am who I am, I’d ask him to define “everybody.” I’d ask whether it included me (it usually didn’t); not because he meant to exclude me, but because he’d never actually said what he meant. He’d said something that pointed toward an explanation and assumed the pointing 👉🏾 was enough.

It wasn’t.

After the relationship ended, I went looking for an explanation of my own. I wanted to understand what was happening in our miscommunication.

So, I ordered a stack of books about ambiguity.

The books approached the topic in the context of linguistics, law, business, ethics, and cognition, not technology. Partway through reading them, I started recognizing a pattern; how people evaluate whether our docs, or an AI answer, is any good.

People who rely on implicit communication — the “everybody knows” move — create the conditions for miscommunication without meaning to. That was obvious to me once I made the connection between ambiguity and understanding that several of the authors touched on. But because of the way my brain jumps from one tangentially-related topic to another, I began to wonder if same implicit-versus-explicit gap in documentation might help (at least partially) explain why AI answer engines produce so-called hallucinations?

Read more

[TL;DR] A taxonomy might look like a neutral list of categories. It’s not. Building one always means someone decides what content earns its own category, what gets lumped together, and what gets left out altogether — and those decisions aren’t neutral.

My friend Rahel Anne Bailie once told me about a taxonomy project she worked on for a European mobile carrier. If I recall correctly, (most days, that’s questionable 🤨), the job was to make an online music library work for listeners in different countries by organizing it so the people in each market could find the music they wanted.

Bailie, founder of Content Seriously and creator of the Content Integrity Model, was helping a client solve the information and data challenges behind a service intended to compete with Apple iTunes. Her team’s work included categorizing the music library, which seems simple, until you try doing it for music lovers in multiple countries.

How Metadata Enters The Equation

Assigning a song to a genre category creates metadata; information that tells a system what something is and where it belongs. Bailie’s team started with a taxonomy shaped by a classification scheme commonly used in the commercial music industry. That taxonomy wasn’t localized for describing music in the new markets Bailie’s client planned to offer the streaming music service to.

👉🏾 Localization is the process of adapting content to be meaningful for a particular culture, locale, or market.

Source: The Language of Localization (XML Press)

To be clear, the categories the music industry used were well-suited for the audiences for which they were built. The problem materialized as Bailie’s team recognized the incongruities in the way music is categorized in various target markets. The solution to the classification problem they faced involved squaring the pre-existing music taxonomy with how each new target market already organized its music. A big challenge given that the original categories hadn’t been designed with this kind of flexibility in mind.

Somewhere in their work, Rahel’s team noticed that world music wasn’t really a genre at all. It was a container: a catch-all category for anything that didn’t fit into existing ones.

Some Countries Organize Music By The Emotion It Evokes

India’s classical music tradition doesn’t organize primarily by genre or geography. It sorts by rasa — the specific emotion a piece of music is built to produce — a system old enough to appear in the Natya Shastra, a text roughly two thousand years old.

Some Countries Organize Music By Color

Traditional Chinese music theory goes further: the five-note scale is mapped directly onto five colors, each note tied to a color, a direction, a season.

In Brazil, the sacred music of Candomblé is organized by orixá — which deity a rhythm calls. Each orixá carries its own color and character, so rhythm, color, and spirit are treated as a single category, not three separate ones.

None of these systems sort music by where it’s from (geography-based music categorization). “World music” doesn’t just group unfamiliar sounds into one bin — it assumes geography-based categorization is the correct way to organize music, an assumption some of the world’s major musical traditions don’t share. That assumption has its own history; and its own paper trail.

It’s not just categories that get stuck at the border. Names can, too. Some of the biggest Western pop stars carry an entirely different identity once they cross into other markets. Britney Spears isn’t categorizes as as “Britney Spears” in China. Chinese fans know her as 小甜甜 (Xiǎo Tiántián), “Little Sweetie,” a nickname with no relationship to a transliteration of her English name. A platform that only indexes her under “Britney Spears” is invisible to anyone searching for 小甜甜.

This Has Been In Heavy Rotation Since 1987💿

The phrase “world music” had existed since the early 1960s, coined by ethnomusicologist Robert E. Brown at Wesleyan University to describe an academic program, not a music marketing category. In 1987, a group of UK record labels met to market a new umbrella term to record stores, so retail shops would have a single bin to store artists that didn’t fit the Western pop and rock mainstream.

Same As It Ever Was, Same As It Ever Was

I was 17-years-old in 1981 when I discovered the song, “Once in a Lifetime,” which turned me on to the new wave band, Talking Heads. Decades later, I was intrigued to find that David Byrne — the Scottish-born American singer and musician best known as the band’s lead singer and guitarist, had something to say about this toss-it-all-in-a-single-bin categorization approach.

It’s clear to me that Rahel’s team didn’t stumble onto a brand-new music categorization problem. Instead, they bumped up against an issue that had been openly discussed by people like Byrne. The lesson here is that taxonomies can (and clearly do) get handed down to others without their history attached. That’s why it’s not surprising that the people applying taxonomy data typically don’t know what assumptions are baked in to the category decision-making process, nor why those choices were made in the fist place.

The Recording Academy has recently revised its award categories in ways that reflect some of the same criticism Byrne highlighted in his commentary. In 2020, it renamed Best World Music Album to Best Global Music Album, explaining that the old name carried connotations of colonialism, folk, and “non-American” music. It also added Best Global Music Performance for the 2022 awards. In June 2026, the Academy announced Best Asian Pop Music Performance, a new category that includes K-pop, J-pop, and C-pop. The broad global categories haven’t disappeared, but the Academy has also begun creating more specific categories alongside them.

The Same Pattern Shows Up In Libraries

This isn’t just a quirk of the music industry: the same categorization pattern appears in a scheme many of us rely on: the Dewey Decimal Classification system. It’s the world’s most widely-applied numerical library classification system, in use in more than 135 countries.

In this system, the category “religion” sits in the 200s. The Bible and Christianity receive most of the range from 220 through 289 (meaning most of the Dewey categorization numbers in the religion section are dedicated to identifying books associated with Christianity). Religious traditions aside from Christianity (e.g., Islam, Judaism, Hinduism, Buddhism, Sikhism, and a variety of Indigenous traditions) are concentrated within 290 through 299, often stacked on top of each other in the same three-digit number.

A 2024 study — using a dataset of more than three million books from a consortium of Ohio academic libraries — found that Dewey’s religion categories show measurably more Western skew than its history or literature categories do, and the Dewey system itself is measurably more biased than the Library of Congress system many academic libraries use instead.

Whatever their intentions, both categorization systems reflected the practical and cultural assumptions and biases of the people who created them. Melvil Dewey built a system that made sense from where he stood, in the late 1800s, and a handful of UK record label executives promoted a system for categorizing music that didn’t fit anywhere else in 1987.

Those who inherited Dewey’s system kept using it without questioning whose perspective it reflected. That’s the reason I wanted a second example here, beyond music. I’m assuming most of you reading this haven’t built a music genre taxonomy, but all of you have organized information for someone other than yourself.

This Problem Shows Up Everywhere

The least surprising version of this problem is ubiquitous and shows up in e-commerce, which is why it’s worth mentioning. Retailers operating across multiple countries run into mismatched categorization constantly. A product that fits cleanly into one country’s taxonomy may be awkwardly placed, duplicated, or missing in another’s. It’s such a routine problem that entire product-information-management vendors exist just to manage it.

Zalando, the pan-European fashion retailer, offers an adjacent example. When the company established a Nordic fulfillment center, it said that being closer to customers would help it provide a more locally relevant assortment, including a larger selection of Nordic brands. The example involves merchandising and logistics as well as information architecture, but the underlying requirement is familiar: a system designed for multiple markets has to account for what people in each market expect to find.

Zalando addressed a related problem by adapting its assortment to regional expectations. It’s also the exception to the rule. Most organizations don’t get there until after the mismatch has already cost them customers, damages market share, or created some other sort of expensive business pain.

What Music, Libraries, And Online Shopping Have In Common

Put the three examples side-by-side and the pattern stops being about music, or libraries, or shopping. It starts being about decisions. A taxonomy looks like a neutral description of what exists. It’s not. Not even remotely. Building one means making decisions — and decisions aren’t neutral.

Someone has to decide what earns its own category, what gets folded into something else, and what gets left off the list entirely.

Dewey already made those decisions when he built out the 200s. Rahel's carrier ran into the same kind of decisions, made by default instead of on purpose. The taxonomy they inherited hadn’t been designed with any other market's categories in mind, and that gap only surfaced once the team started deciding which categories the platform would actually use.

Dewey classified the world’s knowledge based on what he knew and believed back in the late 1800s, and that understanding is still baked into the numbers today. Rahel’s team inherited categories created elsewhere. In Dewey’s case, the creator’s own perspective shaped the system. In Rahel’s project, another organization’s commercial categories arrived already embedded in the work.

You don’t notice a taxonomy’s blind spots until you try to make it work for people it wasn’t built for.

That’s exactly the position Rahel’s team found itself in. Their job was to solve the categorization side of making the platform work across markets, which meant mapping, adapting, or adding categories for each target market — categories the original taxonomy never had. Zalando addressed a related problem by adapting its assortment to regional expectations before customers ever ran into the mismatch.

Every organization that documents, categorizes, or governs information has made these same kinds of decisions, whether or not anyone meant to do so. Support ticket types. Customer segments. Product categories. Content types in your component content management system (CCMS).

Each of those schemas is built around an assumed “normal” case. Nobody likely wrote their assumption down, because the people who built the system already agreed on what normal meant.

Every example in this post so far has focused on a system that organizes information for people to find. The next one organizes information before a person ever sees it.

A Newer Version Of The Same Argument

There’s a newer version of this happening right now, and it’s the one with the highest stakes for anyone who documents things for a living. Increasingly, readers don’t read our docs directly. They ask an AI-powered answer engine a question, and the model that powers it answers using whatever knowledge it has access to.

Sometimes that means retrieving answers from our docs when a question is asked. In that case, labeling, structure, terminology, and other discoverability signals affect whether the system identifies our content as relevant.

Other times, the model answers from patterns learned during training. There, our product info and docs influence an answer only if it was included and retained in the training process in a form the model could learn from.

Either way, it’s the same argument. Metadata helps determine not just how your content is organized, but whether it’s findable, chosen, and understood at all — by a person or by an A-powered tool.

It’s important to note that the corpus of information that models learn from isn’t neutral either. Two influential earlier models illustrate the imbalance: GPT-3‘s training data was roughly 93 percent English, while LLaMA-2‘s was nearly 90 percent. CommonCrawl, one of the largest sources feeding models like these, is roughly 45 percent English — with every other language, including widely spoken ones, trailing far behind.

A 2024 study tested ChatGPT and Mixtral on music specifically. Researchers asked the models to identify leading musical contributors and evaluate the musical cultures of different countries. Both experiments revealed a strong preference for Western music cultures — the same kind of ethnocentrism Byrne criticized in 1999, now appearing in model outputs rather than record-store bins.

An AI training corpus raises a similar problem. It’s shaped by decisions about what to include, not a neutral sample of what exists. The underlying issue is close to what Rahel’s carrier ran into: a system built from one set of assumptions doesn’t necessarily work across every market — or every language — it intends to serve. Now it’s happening to anyone whose documentation gets read by a model instead of a person.

That’s what makes this everybody’s problem: the same decision keeps showing up wherever someone has to sort things into categories, which turns out to be nearly everywhere — a record label, a library, a retailer, and now anyone whose documentation gets read by a model instead of a person.

Rahel’s carrier found out because a new market made the gap impossible to ignore. Zalando addressed a related problem by adapting its assortment to regional expectations. Either way, someone eventually has to ask whether the taxonomy actually works for everyone it needs to serve. Organizations may never ask that question unless someone makes it part of their governance process.

The people closest to the taxonomy — tech writers, content strategists, and CCMS owners — are well positioned to notice those gaps before a customer, a market, or a model exposes them. Not every organization gives someone explicit responsibility for doing so. 🤠

👉🏾 How do we create content AI can reliably understand?
👉🏾 How do we reduce hallucinations?
👉🏾 How do we ensure AI-generated answers are accurate, trustworthy, and consistent?
👉🏾 How do we adapt the practices we’ve spent years developing (structured authoring, taxonomy, terminology management, content reuse, and governance) to an AI-powered world?

This year the conference offers an AI track built around the theme “From Experimentation to Execution: Scaling AI in Content Ops.” While the title references content operations, the sessions themselves are highly relevant to tech writers, regardless of whether they are involved in operational decision-making. In fact, many of the presenters at this year’s event will explore techniques and tactics technical communicators have been practicing for decades, and they’ll explain why those skills have become even more valuable as organizations attempt to deploy AI.

Tech Writers Already Possess The Skills Required To Produce AI-Ready Documentation

One of the strongest messages running through this year’s conference program is that trustworthy AI depends on well-managed content.

Lavacon offers sessions on governed vocabularies, ontologies, taxonomy, structured authoring, workflow management, content modeling, terminology management, content reuse, and global content. Most of these topics aren’t new to tech writers. What’s important to note in today’s fast-changing technology landscape is that these tried-and-true tech writer capabilities are also core competencies that are essential building blocks for trustworthy AI.

A few sessions of interest:

• Brendon Paucek, Senior Content Strategist at Teradata, will show how organizations can establish governed vocabularies and ontologies using tools they already own

• Rob Hanna, CEO of Precision Content, will explain why moving from long topics to modular microcontent makes DITA more AI-ready

• Christi Guzik, Lead Content Operations Manager at Rocket, will discuss building scalable taxonomies

Collectively, these presenters make an important point: AI doesn’t eliminate the need for structured content. It raises the value of producing it well.

Lavacon 2026 Schedule At A Glance

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AI Is Changing How We Produce Docs

Lavacon also helps attendees better understand the AI tools landscape. And, more importantly, that AI platforms aren’t fully-fledged content creation and management tools. They lack the governance, workflow, quality assurance, localization, publishing, and cross-team collaboration capabilities that documentation teams rely on to get work done right.

Here’s a few of the presenters to check out:

• Dawn Stevens, President of Comtech Services, will look at why organizations need operational maturity before scaling AI.

• Luis Fernandez, Executive Director at VML, will argue that poor content models (not content management systems) often are the biggest obstacle to AI adoption

• Karen Brothers, Content Manager at Scott Safety, will discuss what it takes to build an AI-ready content culture

• Amanda Patterson, Senior Consultant at Comtech Services, will examine what organizations can learn from human resources when managing AI agents

Taken together, these sessions move past the novelty of AI and into the operational realities of adopting it responsibly.

Our Docs Are The Knowledge Layer For AI

Perhaps the biggest difference between this year’s program and those of just a few years ago is the number of sessions devoted to AI agents. Rather than asking whether AI agents are useful, presenters are showing how to build them, support them, and supply them with trustworthy knowledge.

Highlights include:

• Emma Pindera, Content Strategist at PointClickCare, presents Creating Content AI Agents That Actually Work

• Patrick Bosek, CEO, Heretto and Megan Gilhooly, Sr. Director Global Content & Intelligence, OneTrust, present Beyond the Bot: How Structured Content Drives an Agentic Ecosystem Across Your Entire Content Stack

• Aman Goyal, Agentic AI Product Manager at T-Mobile, presents Show Me the Agents! Decision Framework for Content Operations AI

• Theresa Daudier, Senior Knowledge Management Engineer at ServiceNow, presents How ServiceNow Used Agentic AI to Reduce Costs and Improve Customer Experience

For tech writers, these sessions reinforce the emerging reality of our profession: our docs are no longer consumed only by people. Increasingly, they’re the trusted knowledge that AI systems retrieve, interpret, and use to answer customer questions.

Trust Matters

If there’s one word that appears repeatedly throughout the schedule, it’s trust.

Speakers explore how organizations can reduce hallucinations, verify AI-generated knowledge, improve translation quality, manage terminology, and preserve human oversight.

• Klaus Fleischmann, CEO of Kaleidoscope GmbH, presents, Terminology: The Clever Cure for Hallucinations

• Kay Conerly, Director of Content Strategy at McGraw Hill, presents, Maintaining Trust in the AI Era: The Enduring Value of Human Expertise in Content

• Ben Cohen of Localize and Nick Wyatt of Aras, present AI Translation in the Real World: Lessons from the Team at Aras on Building Scalable Global Content Workflows

• Sandi Markle of Blueberri, presents, Structure Starts with Trust: Building Cultures That Support Content Systems

These sessions recognize that trust isn’t created by technology alone. Instead, it it earned when we produce well-designed information.

What Skills Will Matter Most?

That question is likely on the minds of nearly all LavaCon attendees. The opening keynote speakers topic selection illustrate how much expectations are changing.

• Maria Romanovsky, Director of Content Design and Strategy at TikTok, presents AI Didn’t Replace You—It Promoted You to Manager

• Keith Boyd, Senior Director at Microsoft, presents Content is Still King: Thriving in the Era of AI

Half-Day Training Workshops

In addition to presentations, Lavacon also offers optional pre- and post-conference workshops (for an additional fee).

2026 Workshops

• Daniel Schomer, How to Build a Content Governance Model

• David Hobbs, Effective Content Inventories and Analysis

• Susan Kelley, From Notes to Knowledge: Learning Faster with Notebook LM (An Interactive Lab for Communicators)

• Gaetan Chretiennot, The “Translate” Button is a Trap: Mastering Translation Risk in an Era of Generic AI

• Sarah Johnson, Operationalizing the Content-First Framework: Diagnose Meaning Drift, Align Teams, and Scale Content with Confidence

• Carol Hattrup, They Already Sent a Poet, and It’s You!

• Leann Long, MythBusters Lab: Let’s Test What Really Works for Remote Content Teams

• Rebecca Schneider, Creating Taxonomies for Your Organization in the Age of AI

• Melanie Davis, Tech Stack Lift-and-Shift: A Tag-Team Approach to Success with Limited Resources

• Dipo Ajose-Coker, Inclusive Content In The AI Era: Bias, Culture, And Who Gets Left Behind

• Rhyne Armstrong, From Shadow to Servant: Confronting the AI Beast

• Amanda Patterson and Brianna Stevens-Russell, Stop Building Bots. Start Hiring AI Employees: How HR Thinking Makes AI Agents Safer, Smarter, and Easier to Manage

Why I’m looking forward to LavaCon

What excites me most about this year’s program is that it reflects where our profession is today.

We’re no longer being asked whether we should pay attention to AI. We’re being asked to help our organizations make AI work. That’s a very different challenge, and it calls on many of the capabilities technical communications pros have spent years refining: creating clear, structured, high-quality, reusable, well-governed content that people can trust.

If you want to see how those skills are being applied across organizations like yours, LavaCon 2026 offers an exceptional opportunity to learn directly from the people doing the work.

Register at lavacon.org, and use discount code TCW at checkout to save 10% on the cost of registration.

Register Today!

I hope you’ll join us in Charlotte this October. I look forward to seeing you there. 🤠

The math was simple and enormous. The company estimated the new drug would earn roughly four million US dollars a day once approved. But the bigger driver was the patent clock 🕰️. Every day the writers spent wrestling with intricacies in the content before submission was a day the drug sat unapproved and unable to be sold. It was also a day closer to the patent’s expiration date. After that, generic makers could copy the formula without doing the research or repeating the safety and efficacy testing the original maker (my client) had already paid for.

That price tag wasn’t small. The company was chasing blockbuster status — ka-ching 💰 ka-ching — industry shorthand for a drug that clears a billion US dollars a year in global sales.

Getting there wasn’t cheap, either. A Tufts Center for the Study of Drug Development estimate put the cost of bringing a new drug to market in that era at roughly $800 million USD. About one-half of that money was direct research and clinical trial spending. The remainder was the capitalized cost of years of tied-up resources and the compounds that failed to become approved products along the way. Falling short of blockbuster sales numbers meant not recouping what the drug cost to make.

Example: Celebrex — Pfizer’s arthritis and pain medication — is the textbook case. Launched in 1999, it was pulling in roughly $2.9 billion USD in sales a year before its patent protection ended in 2014 — resulting in the company losing the bulk of that revenue within about a year once generics arrived in the market.

My client’s IT department was fixated on solving problems with whatever Microsoft software they already had, or whatever they could code themselves with the skills they’d built up over their careers — exactly the same way a man with a good hammer treats every problem like a nail, whether or not it is one.

The writers asked the IT team to help them develop a capability known as systematic content reuse. The “mother of content strategy,” Ann Rockley, coined the term to describe content that is tagged once and automatically published everywhere it belongs. Nobody has to remember where the content lives or go looking for it.

IT heard something different. They thought we were asking for a better way to copy and paste content.

What they knew was the other half of Rockley’s distinction: opportunistic reuse, where someone remembers a paragraph exists, tracks it down, copies it, and then, pastes it in. It’s the most common form of content reuse there is. It doesn’t require any special technology. That’s exactly why the IT team couldn’t see what our tech comm staff was asking for.

What changed their minds was an exercise a trainer named Sally Chase (now retired) ran with the whole group — writers and IT together — training us on the newly-released Arbortext Epic XML editor (one of the first tools built specifically for authoring XML), and on how to build structured XML content designed for systematic reuse following a single-source publishing model. She put everyone in a computer lab, handed out instructions, and had them work through a set of tasks alone, about an hour’s worth. Then she sent everyone to lunch, which in hindsight was the whole point.

When the group came back, she announced she’d handed out the wrong instructions by mistake, and that she’d already removed them from every workstation over lunch. The new instructions, she said aloud, were simple: undo everything the first set had asked them to do.

One IT crew member raised his hand, audibly frustrated. “How are we supposed to remember what you had us do previously,” he said, “when you took the instructions away?”

That was the moment IT understood what a component content management system was actually for. Not software. Memory.

Read more

According to the client, during a string of security incidents this year the tool started giving incorrect instructions that made things worse. For example, the tool told the team to isolate a compromised server using an API parameter that had been retired months earlier. It also walked a customer through re-enabling an encryption setting that the engineers had phased out for compliance last year. And, in another case, it pointed a customer toward what looked like a standard access shortcut — but was not. Instead, the customer was instructed to utilize a leftover authentication bypass built for a sales demo and never removed from the system.

What The Investigation Found

The client told me their engineering team’s first theory was the oh-so-obvious one: the model was hallucinating.

The engineering crew was wrong. After a little detective work, the team discovered that the theory didn’t hold up once someone checked what the AI had retrieved against what was true. To be clear, the information in the hallucination-laden AI generated text was true once-upon-a-time. But, when things changed with the product, that documentation was never updated. Tracing it back to the source turned up technical documentation files that had been sitting there, unmaintained, for quite a while.

Upon closer inspection, they found:

• the retired API parameter still listed in a publicly available integration guide nobody had updated

• the reasoning behind a compliance change that was only once discussed in a Slack thread between two engineers who’d never migrated it to the wiki the AI used as source content — and who both no longer worked at the firm

• the authentication bypass sitting in the codebase with no comment marking it temporary

• the architecture diagrams the model relied on pointing to repositories that had long-since been deleted

On its own, that’s a documentation debt story.

How The AI Reasoned From What It Found

All of the information accessed by the AI was, at least partially, accurate. The facts were fine. It was the absence of explicit cues in the source content and the model’s reliance on that content that led it to generate inaccurate answers.

That’s an inference failure, not a retrieval failure.

Handed the exact same content, a human cybersecurity tech writer likely would’ve stopped to pause and ask perhaps questions of the underlying information. Experienced folks whose day-to-day work provides them with knowledge those with lesser experience lack, likely notice when something looked off, is out-of-date, or otherwise, potentially wrong. It’s background work most of us do constantly without paying much attention to it.

The AI retrieved the same clues as the human cybersecurity tech writer and did not stop to pause and ask questions of the content. It moved straight from “this exists in a document” to “this is the instruction,” with nothing in between.

The Role Nobody Replaced

The client told me something else about that team. Before any of this, they’d had a reviewer everyone relied on — let’s call her Betsy, since her real name isn’t mine to share.

“Everyone relied on her because she was so good at it,” the client told me.

Betsy was, in their words, always the person who’d say, “That doesn’t look right.”

She retired recently. Nobody replaced her with someone who had the same eye for detail. Her responsibilities got split across the rest of the team instead, so proofing and review responsibilities became something everyone was expected to own a piece of — rather than something one dedicated person owned.

She wasn’t simply catching typos. Betsy’s value came from context, product knowledge, and years of experience layered on top of ordinary editing — the kind of background that lets someone recognize when something is off even when nothing is technically wrong. The client’s regard for her was so high that I started thinking of her as the queen of that function. Nobody worried about coverage while she held that role, because she was there.

And then she wasn’t.

Replace Betsy with a system that reads the same docs at machine speed, and the habit of stopping to ask whether something looks right disappears along with her. The documentation debt didn’t get worse. It got a new kind of reader — one with no instinct for stopping to check for accuracy. 🤠

What Claude Was Actually Reading

Claude answered, and its answer confused me. It described the company as serving small and medium-sized businesses in Europe — which wasn’t what I understood the company was trying to communicate. The company was working to expand into the US market, and I knew that going in. So I challenged Claude to explain where that description came from.

Claude then told me what it was actually looking at. The site, it explained, runs as a single-page JavaScript application. The initial HTML response didn’t contain the web page copy a visitor sees after the browser runs the JavaScript.

Some sites work this way by design. The server sends an HTML shell and a bundle of JavaScript; the browser runs that code and constructs the visible page. A retrieval tool that reads only the initial response may never receive the resulting copy. On this site, Claude found the title, the description, and a few other tags — but not the body content. Claude pointed to two of those tags specifically: the meta description and the Open Graph description, nearly identical, one line each — both describing that same European customer base, written for search engines and social-media link previews rather than for a reader trying to evaluate whether to hire this company.

So I copied the entire page — the actual marketing copy, not the URL — and gave that to Claude instead.

From the metadata, Claude described a company that helps European SMEs make their content AI-ready. That wasn’t what the company was trying to communicate. It wanted to reach businesses in the United States and beyond. After reading the page copy I provided, Claude described a broader argument: AI is already consuming a company’s content whether the company prepared it or not, and the quality of that output depends on how well the content is structured. The company delivered this through a staged program — assess, define, transform — that didn’t require adopting a new authoring standard first.

None of those distinctions appeared in the initial HTML. To a tool that never ran the site’s JavaScript, this company was two sentences long — and, on the question of who it served, those two sentences said something the company itself didn’t say.

What This Means

I don’t think the company made an unusual mistake. Many sites render their content in the browser rather than sending it fully formed from the server. Whether an AI retrieval tool can read that content afterward depends on a separate decision: whether the server also produces a readable version for tools that don’t execute JavaScript. That decision is usually made once, by engineering, when the site is built — and rarely revisited by whoever writes or approves the marketing copy. I noticed the gap only because I asked an AI to summarize the company before reading the site myself. Other people evaluating the company could do the same, and get the same incomplete answer.

The stakes here weren’t abstract. The company was working to expand into the US market. I was reviewing it from the US. Claude, working only from the metadata, positioned the company as Europe-focused when it was trying to reach businesses in the United States and beyond. A prospective US customer running the same search, or an AI assistant evaluating the company on that customer’s behalf, could have reached the same conclusion.

The site was built with Lovable, an AI-powered platform that generates websites and apps from written prompts, which makes the cause harder to identify. Lovable now uses server-side rendering by default for projects created after May 13, 2026, built on a framework called TanStack Start. Older projects, built on React and Vite, are pre-rendered on request instead — but only for verified crawlers; human visitors still receive the client-side application.

In this situation, Claude, apparently received that client-side application. I can’t say why. Perhaps the project predated Lovable’s current architecture or maybe the request wasn’t recognized as a verified crawler. Or perhaps the pre-rendering may failed for that request.

Server-side rendering could have made the page’s actual copy available in that initial response. A meta description that matched the page’s actual intended audience — instead of naming the wrong region — would have given Claude a more accurate account even with the body copy inaccessible.

Structured data could have added machine-readable facts about the company (product, for example), but it wouldn’t have exposed the full argument or explained the staged program the company plans to offer. Those details lived in the website copy itself.

The experiment made me wonder who was responsible for the machine-readable version of this company’s pitch — and whether anyone had reviewed it as content.

Tech writers already verify procedures, terminology, and examples before publication. They should also review the page’s title, description, structured data, and the content available before its JavaScript runs.

My experiment began as a positioning review. It ended with two descriptions of the same company: the one prospective customers could see and the one Claude could retrieve. The visible page addressed a broad market. The metadata positioned the company as serving small and medium-sized businesses in Europe.

Nobody signs off on a meta description the way they sign off on website homepage. Metadata often gets copied from wherever it was last written, or generated once and forgotten about. Increasingly, it’s also the only version of the pitch some readers will ever encounter — not because they skipped visiting your website, but because the tool they used to evaluate your company couldn’t access it. 🤠

It attempts to evaluate organizational preparedness across seven pillars. The strange thing is, technical documentation isn’t one of them. That sent me looking for where, exactly, technical documentation should fit into Microsoft’s model, and whether an organization can call itself AI-ready while the documentation feeding its answer engine isn’t.

What Microsoft Means By AI Readiness

Microsoft’s AI Readiness Assessment evaluates whether an organization has the strategy, governance, data, people, infrastructure, and model-management capabilities to adopt AI securely and at scale.

It’s built around seven pillars:

1. Business Strategy

2. AI Governance & Security

3. Data Foundations

4. AI Strategy & Experience

5. Organization & Culture

6. Infrastructure for AI

7. Model Management

The assessment runs on multiple-choice / multiple-response questions. Completing it produces an overall score, category-level recommendations, and links to related guidance. Signed-in participants can export their results to a CSV file.

This assessment approach seems like a reasonable way to size up organizational readiness. Companies looking to put AI to work need leadership support, real safeguards, usable data, decent infrastructure, and people who know WTF they’re doing.

But there’s a question hiding inside those seven pillars that Microsoft never asks directly:

What knowledge is the AI actually going to use when someone asks a question (and expects an accurate answer) about how our products work?

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Then I realized something was different. Those files mostly help systems discover or process information that’s already there. llms.txt exists to help AI systems understand our documentation.

For decades, tech writers have made editorial decisions about what customers should know. We decide which concepts deserve an explanation, which procedures belong (or don’t) in our product docs, and which content should be retired because it no longer reflects reality.

Today, many publishing platforms automatically generate a document whose audience isn’t your customer at all. Instead, it’s AI.

This Isn’t Just Another Generated File

An llms.txt file is often described as a guide for AI systems. That’s accurate, but incomplete. It’s much more than that. You can think of it as a summary of what your organization considers important enough for AI to read first.

Depending on how your documentation platform generates it, the file may point to product overviews, API references, tutorials, SDKs, migration guides, code examples, and other documentation. An AI system can use that information to build an initial understanding of your product long before it retrieves content from any individual page.

That’s a different editorial function than a sitemap.

👉🏾 A sitemap says, “These pages exist.”

👉🏾 An llms.txt file effectively says, “Start here.”

Automatic Doesn’t Mean Curated

Recently, I examined the llms.txt file for a public API documentation site. Nothing in it was confidential. Every page it referenced was already public.

What surprised me wasn’t the content. It was how much easier the file made it to understand the product.

Instead of exploring the documentation one section at a time, I could immediately see the major APIs, supporting guides, integration points, and product organization. The documentation had been distilled into a machine-readable map.

That’s exactly what the file was designed to do.

The question is whether that map reflects an editorial decision or simply the default output of a publishing platform.

Those aren’t the same things.

An automatically generated file may include deprecated documentation, legacy APIs, archived migration guides, experimental features, or transitional content simply because those pages are still public. The software doesn’t know which content best represents your product today. It only knows what we tell it.

This Is An Editorial Question

Tech writers are the people who decide how a product should be explained. We choose examples, organize information, and retire obsolete content. We often work with product managers, software developers, support teams, and legal reviewers because we understand that publishing is an editorial process, not a database export.

I think llms.txt belongs in that process. If AI systems are going to use it as a starting point for understanding our documentation, then someone should review what it communicates before its published.

Not because AI shouldn’t read your documentation. Because we should be intentional about what we’re helping it understand.

A New Publishing Checklist

Most documentation teams already review titles, links, examples, screenshots, and metadata before publishing. It may be time to add one more item to that checklist.

👉🏾 Review the documentation we’re publishing for machines with the same care we give the documentation we’re publishing for people.

A publishing platform can generate an llms.txt file automatically. Only we can decide whether it tells the right story about our product. 🤠

Note: The llms.txt specification is evolving through community input. Its GitHub repository provides version control, issue tracking, and public discussion, while a community discord channel gives implementers a place to share experiences, compare approaches, and discuss emerging best practices.

Our documentation is still doing its job, however, the people we’ve been creating those docs for may never actually visit our doc portal this time around. That’s an enormous shift that wasn’t much of a consideration only a few short years ago.

Why Should Tech Writers Care About Zero-Click Searches?

Tech writing pros should care about zero-click searches because the decisions we make change how our customers discover and use our docs.

More people are turning to AI chatbots and AI-powered search for information, and many accept the answer without visiting a source page. Pew Research Center found that 42% of U.S. adults use chatbots to search for information and 60% read AI summaries in search results. When Google displayed an AI summary, users clicked a link within it in only 1% of observed visits. For documentation teams, the implication is clear: product info can reach customers through an AI assistant, AI-generated search summary, or support application even when they never visit the documentation site where it was published.

That means traditional doc portal traffic reports no longer meaningfully reflect real-world usage. Zero-click searches also increase the importance of clear writing, consistent terminology, structured content, metadata, and governance because those elements directly influence the quality of the answers AI systems generate about our products.

Related reading: The Impact of Google AI Overviews on Publisher Traffic and User Experience: Evidence from a Field Experiment

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Rather than guess, she called customer support. The representative who served her told her to enter the Policy ID.

My mother replied:

“If they’re the same thing—and to be clear, they’re not—I wouldn’t have needed to call you in the first place.”

The representative knew immediately why my mother was frustrated and which number she actually needed. My mother didn’t.

Why Insurance Forms And Cards Use Different Terms

Typically, these mismatches arise because the form, insurance card, and customer support content are each built and maintained (well, sometimes 😎) by different teams (often using different software) with no shared single source-of-truth connecting their terminology.

Each team labels the same identifier within its own system and for its own purposes, which can be — and often are — different. Nobody owns the relationship between the differing labels.

Is This Just An Insurance Industry Problem?

No, it isn’t. I’ve seen the same disconnect in software licensing terms, telecom account numbers, HR benefits portals — anywhere a form asks for one thing and the source document hands the customer another.

My mother’s insurer isn’t unusual in this respect. In my own consulting work, I’ve repeatedly encountered the same pattern: organizational structures that divide work into departments tend to produce siloing, and the mismatched results of that situation often produce exactly this kind of labeling problem. When the results get big enough (inconvenient, painful, expensive enough), I call them content hairballs, a term I picked up from Ray Gallon, co-founder of The Transformation Society, who once described organizational content as a tangled mass that different teams and disciplines each get their hands into without anyone owning the whole.

Is This One Insurer’s Mistake Or An Industry-Wide Pattern?

I didn’t need to investigate whether this was unique to my mother’s insurer. Experience had taught me it wasn’t. What I wanted to know was whether people were talking about it publicly, and they are.

As one explainer puts it, insurers aren’t consistent in how they label these numbers, and it routinely tells people to call customer support if they’re not sure which number applies. That guidance exists because people keep asking the same question online — strangers, in public, running into the same confusion my mother did.

That phone call to support isn’t an exception. It’s the documented, expected way the ambiguity gets resolved in far too many organizations today.

What This Reveals About Product Info As A Business Asset

The relationship between Member Number and Policy ID has already been resolved once — by a customer support representative who knew the answer. That resolution doesn’t live anywhere a customer (or an AI system) could find it on their own.

My mother’s support call didn’t happen because information was missing. It happened because the relationship between the two terms was never made explicit where she needed it. Customer support teams have been consistently bridging gaps like that for years, absorbing the cost of inconsistent terminology one phone call at a time. ☎️

AI systems reason from whatever product information organizations make available to them. When the relationship between two terms is implicit there, AI inherits the same ambiguity customers have been living with for years. An AI assistant can’t ask a colleague which label the organization really intended — it can only reason from the evidence it’s given, and it doesn’t always do that correctly.

What This Means For AI Readiness

AI readiness may start earlier than most organizations assume; with terminology management, before any model, prompt, or retrieval system gets involved. If AI systems depend on someone having resolved relationships like Member Number and Policy ID, what else has customer support been compensating for that no one ever wrote down? 🤠

“What I really want is a standard, recognizable symbol,” they told me.

Something like the Compact Disc Digital Audio logo or ENERGY STAR, the US government-backed mark identifying products and buildings that meet defined energy-efficiency standards. A writer could place it on printed or web content to tell readers that a person created what they were about to read.

I understood the appeal. Readers increasingly have trouble determining how an article was produced. Writers who create their own material have little more than a byline with which to describe their process. So I went looking for the mark and found something interesting.

A Standard Built On A Human Declaration

Micah Voraritskul founded VerifiedHuman in April 2023. The organization now includes certified creators across more than 25 countries and six continents. Its eight categories cover writers, visual artists, musicians, voice actors, content creators, educators, organizations, and advocates.

Membership is pay-what-you-can, with no required minimum. Members agree to a published standard and may place the VerifiedHuman mark on individual works that meet it. The mark doesn’t cover everything a creator produces.

I expected the process to examine the work before issuing the mark. Instead, creators receive immediate access to the certification marks after joining and agreeing to the standard. No work is independently reviewed as part of that initial process.

A creator completes a short questionnaire (I signed up to see what it asked) and represents that qualifying work meets the requirements. The mark initially communicates that declaration.

VerifiedHuman describes its system as “trust-based, process-driven approach rather than automated detection or probabilistic analysis.” That disclosure matters because some of the language surrounding the mark suggests something stronger.

How Does VerifiedHuman Classify Human And AI Involvement?

VerifiedHuman bases its framework on a five-level classification system.

The organization argues that asking whether someone “used AI” no longer tells us enough. A writer may use AI to brainstorm, organize research, create an outline, draft passages, or produce an entire article. Treating every use as equivalent conceals what the writer contributed.

The Human-AI Collaborative Spectrum defines five levels:

👉🏾 VH5 covers work created entirely by a person without generative AI contributing to the ideas, drafting, or production.

👉🏾 VH4 allows generative AI to assist with research, outlining, brainstorming, and similar supporting tasks, while the creator produces the work.

👉🏾 VH3 allows AI to generate raw material, including drafts and passages, provided the creator substantially transforms it through their own voice, structure, perspective, and editorial decisions.

👉🏾 VH2 describes work largely generated by AI and then directed, selected, or lightly edited by a person.

👉🏾 VH1 covers work generated by AI with little human involvement beyond prompting and accepting the result.

The boundary falls between transformation and editing. It asks who controlled the resulting work and what that person contributed.

What Work Qualifies For The VerifiedHuman Mark?

The site illustrates the difference between VH3 and VH2 with two writing scenarios.

In its VH3 scenario (which is above the threshold and qualifies the creator to use the mark), a writer begins with an AI-generated draft but rebuilds the argument, replaces generic material with original examples and analysis, and rewrites the piece in their own voice. Little of the original generated composition remains.

In its VH2 scenario (which is below the threshold and does not qualify the creator to use the mark), someone generates a complete post, makes minor changes for accuracy and tone, and publishes it. The system supplied the structure, voice, and other creative choices that define the finished piece.

Each person directed an AI tool and reviewed the result. The standard classifies the work according to how much of the generated composition survived and whether the writer imposed their own creative decisions on it.

VerifiedHuman also offers writers a gut check called the Five-Word Principle:

“If you cannot change five consecutive words from an AI draft without breaking the meaning,” the site says, the material hasn’t become your own.

The site doesn’t present this as a formal test, and it couldn’t function as one. The ability to rewrite five words doesn’t establish who originated a passage. A capable editor can rephrase machine-generated prose without becoming its author. A writer might also retain five ordinary words while replacing the argument around them.

The principle is more useful as a question about dependency. Are you expressing an idea you understand and control, or preserving the language and arrangement delivered by the system because the paragraph stops working when you disturb them?

The broader VH3 standard considers more than five words. It examines the voice, structure, perspective, examples, and analysis in the finished piece. Demonstrating an ability to paraphrase isn’t enough. The writer must take control of the composition, which is the distinction I decided to test against my own work.

Would My Writing Here Qualify For The VerifiedHuman Mark?

Quick answer. Maybe, probably, certainly not.

I use AI assistance when I write, including while developing this blog post. I work with an AI collaborator under a long editorial operating manual I wrote. The manual covers voice, evidence, sourcing, structure, and revision.

I supply the observations, map out the story arch, identify the key outcomes, and determine what needs investigation and why. I check claims against primary sources, reject drafts, change the direction, challenge assertions, and make the editorial decisions. I also typically introduce some snarky commentary that many of my readers have come to expect.

Under the VerifiedHuman standard, those decisions count. But, they don’t mean I qualify to use the mark.

If I directed the research and approved every claim but retained much of the AI-generated phrasing and structure, the piece could fall below the VH3 threshold. If I rebuilt (significantly rewrite) the material using my own language, arrangement choices, examples, and analysis, it could qualify.

As we writers already know, that earlier work typically involves more judgment than composing sentences. We may determine the subject, notice a contradiction, identify the evidence, reject unsupported claims, and decide what our articles should conclude. Another system may help turn our decisions into easy-to-process prose.

VerifiedHuman counts those contributions. Its criteria include the ideas, structure, perspective, and creative direction behind the writing. But directing and approving the work aren’t enough if much of the AI-generated language remains. Under those criteria, some of my writing wouldn’t qualify to display the mark.

How Does VerifiedHuman Compare With Independent Certification?

VerifiedHuman describes its model as “like Fair Trade for creativity.” The comparison makes sense, at first. Both use a voluntary standard and a recognizable mark intended to help people make choices based on how something was produced.

Fair trade compliance is established through independent audits conducted by FLOCERT. Auditors inspect operations, review records, require corrective action, and conduct unannounced audits when allegations or other risks arise.

VerifiedHuman’s initial certification works differently. Creators receive access to the mark after agreeing to the standard, without an independent review of a particular work.

Its certification-standard page describes two types of verification:

• Passive verification begins when someone raises a concern about certified work. VerifiedHuman then questions the creator about the tools and process used.

• Active verification involves third-party auditors evaluating random samples, particularly from educational institutions and organizations. The site doesn’t say how often active verification occurs or whether individual creators may also be sampled.

The difference makes the word verified difficult to ignore. The page for writers says that “writers need to prove their work is not AI.” Agreeing to the standard doesn’t independently provide that proof, although a later review may examine the creator’s process.

I don’t read the contradiction as an attempt to mislead. The site is unusually direct about its trust-based process. The stronger language reflects a problem VerifiedHuman cannot solve because no one else has solved it either.

Can Text Alone Confirm Human Authorship?

Researchers have found no reliable method that can examine finished text alone and determine that a person authored it. AI-detection tools claim to identify generated writing, but they can misclassify human writing, with especially high error rates documented for some groups of writers.

For example, SubStack (the platform I use to publish this blog) uses an AI-writing detection tool from a company Pangram that says it’s “An AI detector that actually works” and “detects AI-generated content with 99.98% accuracy.”

Chatlh! — that’s Klingon for “nonsense”. AI-assisted me in crafting the post in the screenshot below — Pangram rated it as 100% human. It’s not.

While I believe they know what human means, maybe they don’t know what 100% means. 😎

Content Provenance Matters

Content provenance records verifiable facts about the history of digital content. It doesn’t tell us whether the content is true, accurate, or valuable. Instead, it gives us more evidence to consider when making that judgment.

VerifiedHuman says it became a contributor member of the Coalition for Content Provenance and Authenticity, or C2PA, in January 2026. C2PA specifications support cryptographically signed information about a file’s origin and editing history. That information can identify recorded actions and the tools involved.

It cannot establish who supplied the thinking behind a sentence.

VerifiedHuman offers a published standard, a creator’s initial declaration, and accountability procedures that may later examine the work. The mark itself doesn’t tell readers whether that examination occurred. A declared work and one reviewed by a third party appear to carry the same mark.

What Does The VerifiedHuman Mark Mean?

The writer I spoke with wanted a mark that could confirm human authorship. The VerifiedHuman mark alone cannot do that. It tells readers that a creator has agreed to a defined framework and represents that the labeled work meets it, but it doesn’t indicate whether anyone independently examined that claim.

The declaration relies on the creator telling the truth, a limitation VerifiedHuman acknowledges. Bylines have always involved some degree of trust. GenAI has complicated that trust by raising new questions about what writers should disclose to readers about how they produce their work.

The spectrum moves the discussion beyond the blunt question, “Did you use AI?” It describes what writers actually contribute and also brings us to a boundary the mark cannot define on its own.

The VerifiedHuman mark gives writers a standard way to disclose how they produced an article. However, reviewed and unreviewed work use the same mark. Readers can see what the writer claims about the process, but they cannot tell whether an independent party checked that claim. 🤠

What are your thoughts on this approach? Share your thoughts in the comments section.

That advice has been retired by Google, but the exact same content model is now being promoted for Answer Engine Optimization (AEO) by a host of others, and I wanted to know whether the new claim is supported by comparable evidence.

What Answer Engine Optimization Aims To Accomplish

AEO is the effort to improve how often and how accurately an organization’s information or brand name appears in answers generated by AI systems. AEO includes AI features in search engines, but it isn’t limited to Google or Bing. It also applies to answer engines that search or retrieve information from the web and use what they find to generate a response.

AEO success can mean several things. Your organization might be mentioned in an answer or your information might be used without attribution. A page might receive a citation or link and someone might follow that link, visit the website, and take a business action.

What I want to know if this: Does organizing content around explicit questions followed by concise answers improve the chances that an AI answer engine will retrieve, use, or cite that content? And is the evidence strong enough to justify changing a website’s content model?

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If the phrase “killing our planet” immediately caused you to mutter something about alarmists, hang with me for a minute.

Patrick Bosek and I invited Gerry to be a guest on our talk show, Coffee and Content, in 2023 to discuss digital waste, content management, and the hidden environmental costs of the information organizations create and store. Gerry isn’t interested in scoring political points. He’s interested in waste, and he makes his case with research.

As it turns out, unused slide deck presentations don’t care who you voted for. 😎

One statistic in this book stopped me. McGovern argues that as much as 90 percent of digital data is never used. In an interview with Nexer Digital, his advice was simple:

“If you have data that you no longer need that is stored on your phone or laptop, get rid of it.”

In a separate conversation with Silicon Republic, he put the underlying problem more bluntly:

“Digital is not green. Digital is electrical, meaning that everything we do in digital consumes energy and creates pollution.”

I remember nodding along when Gerry talked about this during our interview. Then I spent the next couple of years faithfully preserving slide deck presentations I hadn’t opened since before the Obama administration.

The Cleanup

Eventually I decided Gerry deserved at least one obedient reader. 😎 So I started cleaning: old hard drives, cloud folders, archived conference materials, photographs, music, presentation decks I’d accumulated over the better part of three decades. My goal was simple — delete the ones nobody would ever miss and stop contributing quite so enthusiastically to the digital landfill.

Like most cleanup projects, this one wandered off the original assignment.

I found a 2013 talk from tcworld India called “It’s All About Structure,” a September 2015 Content Marketing World session in Cleveland called “Creating a Digital Content Factory: Getting Started with Intelligent Content,” and a 2017 Meetup talk in San Francisco I delivered called “The Cognitive Era and the Future of Content.”

Different years, different audiences, different vocabulary. Opened side-by-side, none of them were about content, really. They were about whether an organization could keep making sense of what it knew as the volume of what it knew kept growing.

The Scalability Problem

One of the oldest presentations took me back to work I did with the “mother of content strategy,” Ann Rockley, at a pharmaceutical company. For years I’ve described that project as documentation work. Looking at those old slides, I’m not sure that’s the best description anymore. The docs weren’t even the actual problem. The business kept changing, and the information had to change with it just to stay accurate. That’s not a writing problem.; it’s a scalability issue.

The 10x Lens

A few years later, another influence sharpened the question. In 2018, I joined OpenExO as Interim Director of Content. The organization was founded by Salim Ismail, whose work on exponential organizations was built around a simple provocation: stop asking how to get 10% better and start asking how to get 10 times better. Patrick and I later interviewed Salim on Coffee and Content, and through that work I found myself reading Peter Diamandis, Ray Kurzweil, and others writing about exponential growth.

Most of those conversations were about organizations scaling exponentially. Mine kept drifting toward information. If a company could get 10 times better, could its knowledge? Could the systems it used to create, store, and make sense of information grow the same way — without quality, meaning, and trust collapsing under the weight of it?

The Same Question, Different Vocabulary

I thought my career had moved through a series of different subjects: structure, marketing, customer experience, exponential organizations, and now AI. The old slide decks showed me something else. I’d been examining the same scalability problem from different angles the whole time.

I started that afternoon trying to reclaim a little storage space. What I found instead was a paper trail showing the same question asked in different vocabularies, going back further than I’d realized.

Why It Matters Now

That’s probably why AI has never felt like a dramatic career pivot to me. I was calling it “the cognitive era” back in 2017, when most of the industry was still talking about big data.

For twenty-five years, I’ve been asking how organizations can scale the production and use of information without letting quality, meaning, and trust collapse. AI didn’t create that interest. But it certainly expanded the audience for the information, and it raised the stakes.

I don’t know that I’ll ever stop asking that question. 🤠

In his most recent work, 99th Day: A Warning About Technology (February 2026), Gerry McGovern examines the human and environmental damage hidden beneath our digital economy. Through research and stories from affected communities, he connects AI, data centers, cryptocurrency, mining, and electronic waste to a culture of unchecked growth and extraction. The book challenges promises of a “green” technological future and asks what must change if we’re to leave a livable planet for future generations.

I had described the same workflow differently in two places. Some of the business rules didn’t become apparent until Claude reached points where the process couldn’t continue without them. Guidance I had developed over time conflicted in subtle ways that I hadn’t noticed because I already knew what I meant, but Claude didn’t.

Each time Claude reached one of those points, we stopped and worked out what the process was supposed to be. Sometimes that meant reconciling conflicting language. Other times we reorganized the information because the structure no longer held. A few problems forced us back several steps.

Looking back, we didn’t spend most of our time building software. Instead, we tried making the work explicit enough that Claude could apply it.

Why Did This Feel So Familiar?

A few weeks later I read a paper from Harvey, makers of AI software for legal pros, about governing AI agents. One sentence stopped me.

“Agents do not change what governance is for. They change where governance happens.”

A few pages later, another passage:

“Before automation can produce consistent results, the underlying process has to be understood, documented, and made repeatable.”

That described what had been happening more clearly than I had.

I wondered whether Harvey was unusual.

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Employees across content teams are already using ChatGPT, Claude, Gemini, Copilot, and other AI tools to write, edit, summarize, and research information. In many places, that work is happening without shared standards, governance, or even a clear understanding of which tools are in use and by whom.

Drawing on findings from the 2026 AI Use in Technical Documentation Survey and insights from industry experts, the BYOAI (Bring Your Own AI) Report examines what this shift means for content teams and offers a practical roadmap for bringing AI use under control at work without slowing down innovation.

Unapproved AI tools are becoming part of everyday work. Request a copy of the report to see why content teams embrace shadow AI and how companies can replace fragmented workflows with secure, governed AI adoption.

What’s inside:

• What Bring Your Own AI (BYOAI) is — and why banning it never works

• The risks already showing up in your content —inconsistent output, quality gaps, no shared baseline

• The risks you won’t see until something goes wrong —data leakage, platform instability, EU AI Act exposure

• Why the fix is prompt consistency, not tool policy —and how a shared prompt library makes it real

• A five-step roadmap from ad hoc to governed AI — week-by-week action plan

• Thoughtful commentary — from content industry professionals

This eBook is brought to you by The Content Wrangler, and the AI prompt management and engineering platform, Promptitude.

Request Access

There was an agenda, several bullet-heavy slides, a comparison table, a few icons, and the familiar blue background that seems to accompany many enterprise software slide decks.

There wasn’t anything obviously wrong with the slides it generated. If someone had shown it during a conference presentation, I doubt anyone in the audience would’ve complained.

At the same time, I couldn’t point to a single slide that helped me make sense of the topic. The deck looked like a presentation because the AI tool had learned what presentations (slide decks it was trained on) typically look like. That isn’t the same thing as understanding how presentations are designed and what techniques are needed in order to communicate with slides effectively.

Why Do AI-Generated Slides Feel So Familiar?

This question set me off on a hunt for guidance from an old school resource. My bookshelf, which housed several works from two communication design influencers.

Edward Tufte hardly needs an introduction for many readers of The Content Wrangler. But for the uninformed, he’s Professor Emeritus in Political Science, Computer Science & Statistics at Yale University. His books, especially The Visual Display of Quantitative Information, Envisioning Information, and Beautiful Evidence, shaped the way generations of technical illustrators, information designers, and technical communicators think about charts, diagrams, comparison, and evidence.

Carmen Simon comes from a different world. A cognitive neuroscientist, she studies attention, memory, and decision-making, asking what people notice, what they remember, and what ultimately changes their minds. Books on my bookshelf authored by Simon include, Impossible to Ignore: Creating Memorable Content to Influence Decisions, and Made You Look: How to Use Brain Science to Attract Attention and Persuade Others.

To be clear, neither of these experts’ books contained advice about AI presentation tools. Instead, Tufte asks whether the presentation makes important relationships visible. Simon asks whether the presentation directs attention to what matters.

Those ideas gave me a different way to look at the slide deck AI produced. The deck generated resembled thousands of presentations I'd seen before, but I’m fairly sure the deck hadn’t been designed to help audiences understand any of the ideas contained in it.

AI-powered presentation generators have learned the patterns found in millions of previously published slide decks. That doesn’t mean they’ve learned which presentation choices actually help people understand information. This observation became more obvious after a small experiment.

What Happens When AI Tries To Communicate Ideas?

Simon uses a comparative visualization (like an elephant standing next to a mouse) to communicate an enormous difference in size. It’s a simple example because everyone already understands the comparison before the speaker explains it.

Comparative Visualization Approaches And Example

539KB ∙ PDF file

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Curious what AI would do, I asked an image generator component of ChatGPT for the same scene. The elephant looked terrific. The mouse, however, came back looking like a mouse, but much too large.

The image generator produced a balanced, attractive image, but it appears to have solved a different problem than the one I was trying to address. Simon's example isn't really about creating an attractive image. It's about making the difference in size immediately obvious, as in the image below.

Edward Tufte made a similar point years before generative AI existed. In The Cognitive Style of PowerPoint (eBook), he argued that presentation software often encourages people to reduce complicated ideas to outlines and bullet hierarchies, writing that:

“PowerPoint routinely disrupts, dominates, and trivializes content.”

His criticism wasn’t about the Microsoft app. It was about what happens when presentation conventions become more important than the relationships we’re trying to explain.

That may also explain why so many AI-generated slide decks feel strangely familiar.

We sometimes criticize AI-generated presentations for looking too much like PowerPoint. In reality, PowerPoint may have been teaching AI how to make presentations. If we don’t like some of the AI-generated results, we should probably spend at least a little time looking in the mirror.

What Can Tech Writers Learn From This?

None of this feels particularly foreign to tech comm pros because we’ve been making communication decisions for years. We decide whether something belongs in a table instead of a paragraph, whether a diagram will explain the idea faster than prose, whether the warning belongs before the procedure instead of after it, and whether two ideas should be compared side-by-side rather than described one-after-the-other.

Those aren’t formatting choices. They’re communication choices.

AI presentation tools appear to recognize presentation patterns more readily than communication objectives. That's why human review still matters. Someone needs to decide whether each slide helps the audience notice, compare, understand, or remember what matters.

Can Better Prompts Produce Better Slide Deck Presentations?

One thing surprised me while experimenting with these tools. Changing the prompt often had more impact than changing the AI-powered presentation generating software.

Asking Google Gemini for a:

“presentation slide deck about structured content aimed at tech writers”

produced a perfectly ordinary deck (below).

Mastering Structured Content — Gemini Basic Prompt Experiment

1.99MB ∙ PDF file

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Asking the system to:

“help tech writers understand why AI produces better answers when information is well structured”

changed the character of the presentation because the prompt described the audience and the communication objective instead of just the topic.

The Ai Structure Synergy

1.84MB ∙ PDF file

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I altered the prompt to provide additional instruction and context:

“Design a presentation that introduces technical writers as “context designers” for an audience of technical communicators and documentation leaders.

Apply communication principles derived from the work of Edward Tufte and Carmen Simon rather than simply reproducing common presentation styles.

For each slide:

👉🏾 Define a single communication objective before selecting any visuals.

👉🏾 Use images only when they help the audience understand, remember, or compare an idea more effectively than text alone.

👉🏾 Prefer visual comparisons, juxtaposition, scale, process, or cause-and-effect relationships over decorative illustrations or generic stock photography.

👉🏾 When appropriate, deliberately sacrifice visual symmetry or aesthetic balance if doing so makes the intended relationship easier to perceive.

👉🏾 Keep text concise and allow the visual to carry as much of the explanation as possible.

The presentation should compare the traditional technical writer role with the emerging role of the context designer. Show how responsibilities shift from producing documentation to designing the information environment that enables AI systems and humans to retrieve, interpret, and apply knowledge correctly.

For each slide, provide:

👉🏾 the communication objective
👉🏾 the key message
👉🏾 the recommended visual
👉🏾 why that visual supports comprehension according to the communication principles being applied
👉🏾 the accompanying slide text

Do not optimize for attractive slides. Optimize for rapid comprehension and long-term recall.”

That makes me wonder whether prompt engineering will gradually become less about describing deliverables and more about describing communication objectives.

Hmmm…

How Should We Evaluate AI-Generated Presentations?

Most reviews of AI presentation tools focus on how quickly they generate slides or how polished those slides look. Those don’t seem to be the questions that matter most. I’d rather know whether the presentation makes important relationships obvious, directs attention where it belongs, and leaves the audience understanding something they didn’t understand before they walked into the room.

I’m putting together a simple evaluation framework based on Tufte’s work on information design and Simon’s research into attention and decision-making. The plan is straightforward: generate the same presentation with several AI presentation tools, score each deck against the same criteria, and see what happens.

I honestly don’t know which system will perform best. That’s exactly what makes the experiment worth running.

We’ve spent years thinking about clarity, comparison, emphasis, and understanding. AI hasn’t made those questions less important. If anything, it has given us another reason to ask them. 🤠

These jobs are also becoming some of the most important work documentation teams do today. As AI assistants increasingly answer customer questions using our technical documentation, knowledge bases, and support content, they don’t distinguish between information that’s carefully maintained and managed and information everyone forgot (or never knew) had been published online. They retrieve whatever is available — accurate or not.

That means governance is no longer just an internal publishing concern. It’s become one of the factors that determines whether AI gives customers accurate answers.

What Is Content Governance?

Content governance defines how information is managed throughout its lifecycle. It establishes ownership, review schedules, approval workflows, publishing standards, metadata practices, and archiving and retirement policies so our docs remain accurate as our products evolve.

Governance work often sits behind the visible parts of documentation, which makes it easy to overlook until conflicting or outdated information reaches a customer. The value isn’t in the process itself. It’s that our customers never have to wonder which of our installation guides is current or whether two procedures describing the same task are equally valid.

AI depends on those same signals.

Why AI Raises The Stakes

Traditionally, our prospects and customers searched documentation one topic at a time. If they found conflicting information, they often recognized the inconsistency and continued searching until they found a better answer.

Retrieval systems approach the problem differently. Before generating a response, they may retrieve info from product documentation, release notes, support articles, troubleshooting guides, FAQs, and knowledge base content. The model then generates a response from the retrieved context, even when that context includes sources that disagree.

When documentation is well managed, that process usually works well. But, when a variety of sources describe the same feature differently, the answer generator may confidently blend information that should never have appeared together.

That’s often described as an AI problem. In reality, it’s frequently a content governance problem that AI has exposed.

AI Doesn’t Know Which Docs Everyone Trusts

Documentation teams usually know where the source of truth lives. They’ve learned through lived prior experience. Software doesn’t have any such awareness.

Imagine the engineering team updates the installation guide, the customer support staff publishes a temporary workaround, product marketing simplifies the feature description on our web page, and an older version of the docs remains publicly accessible because removing it might break existing links. Uh-oh.

An experienced tech writer can quickly identify which docs reflects the current product. A retrieval system, however, sees several differing sources of information discussing the same topic. Unless ownership, metadata, publishing controls, and lifecycle management clearly identify the authoritative version, it has little basis for deciding which information deserves the most confidence.

Publishing: One Step In The Content Lifecycle

Publishing used to mean our docs were ready to be read by our prospects and customers. Now it also means it’s ready to be retrieved by machines.

Once our content is published, AI assistants, enterprise search platforms, support chatbots, and answer engines can begin indexing and retrieving it. They may continue using that information long after our product has changed unless someone — us — keeps our content current.

That’s where governance comes in. Review schedules, content audits, ownership, metadata, and retirement policies don’t just help our documentation team stay organized. They reduce the chances that outdated or conflicting information remains available for retrieval. They won’t make an AI model smarter, but they can make the information it retrieves substantially more reliable.

Governance Improves AI Without Changing The AI

Most organizations won’t build their own large language models. They’ll use commercial AI systems that retrieve information from existing documentation.

That shifts the conversation in an interesting direction.

If answer quality depends heavily on the information being retrieved, then improving AI doesn’t always begin with changing the AI. It often begins with improving the content ecosystem feeding it.

Clear ownership reduces abandoned documentation. Consistent terminology reduces ambiguity. Regular reviews reduce stale content. Controlled publishing reduces conflicting versions of the same information.

None of those improvements requires a larger model or a better prompt.

They require better documentation operations.

Regulated Industries Have Been Preparing For This For Years

Organizations in healthcare, financial services, aerospace, defense, manufacturing, and life sciences (and other industries with concern for avoiding liability and ensuring regulatory compliance) don’t invest in governance because they enjoy documentation reviews. They do it because inaccurate information creates preventable financial, regulatory, and safety risks.

AI expands that business case.

An outdated dosage recommendation, compliance requirement, installation procedure, or configuration step no longer has to be discovered by someone digging through archived documentation. If the content remains available, AI may retrieve it.

Even organizations outside heavily regulated industries face a similar challenge. Customers generally don’t distinguish between an incorrect AI response and incorrect company information. From their perspective, the company gave the answer — and it was wrong!

Governance Is Part Of The Customer Experience

For years, governance existed behind-the-scenes. Documentation shops relied on it to manage change, assign ownership, retire obsolete content, and keep growing collections of information accurate over time.

Those same practices now affect every answer AI generates from our content. Long before our customer asks a question, governance has already determined whether the AI finds accurate, current information or outdated and conflicting documentation.

Customers don’t know — or for that matter, nor do they care — whether an answer came from our documentation portal, support chatbot, or an eager-to-please AI assistant. They assume it came from us. When the answer is wrong, they’re unlikely to blame the content governance process (or lack thereof). They’ll blame our brand.

That’s why governance has become more than an internal publishing discipline. It’s one of the ways organizations improve the quality of the answers AI delivers without changing the AI itself. 🤠

Why Does API Documentation Drift?

API Docs drift because software changes faster than every source describing it can be updated. Each update to a release introduces changes. An endpoint is added. Maybe a parameter changes or an software development kit (SDK) gains a convenience method. Our release notes might announce a new capability, while our tutorials and examples, are (maybe) updated later — or not at all.

These are typical situations and they rarely reflect poor documentation practices.

The information describing an API is often maintained in different repositories, formats, and is managed by different teams sometimes using differing approaches and tools. Small differences accumulate until the implementation, OpenAPI specification, SDKs, release notes, and published documentation no longer describe exactly the same product.

Many organizations don’t discover those inconsistencies until customers encounter and report them.

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