I want to tell you about the last time I read official documentation.
It was a Wednesday. I was integrating a third-party payment API. I opened the docs, navigated four levels of sidebar hierarchy, found a page that hadn't been updated since the previous major version, and spent forty minutes reading an explanation of a concept I already understood in order to find the two lines of configuration I actually needed.
I closed the tab. I opened a chat window with an AI model. I typed: "I'm integrating Stripe webhooks into a Node.js Express app with TypeScript. I have a raw body parser already configured. Show me the minimal setup to verify webhook signatures and route events to handlers."
Twenty seconds later, I had exactly what I needed. No navigation. No context-switching. No deprecation notices for things I wasn't using. Just the answer to my specific question, in the context of my specific stack.
I have not opened a documentation page since.
Why AI Explanations Are Actually Better
I want to be precise about this, because I'm not making a vague "AI is good" argument. I'm making a structural argument about how knowledge transfer works in software development.
Official documentation is written for a statistical reader — someone with an unknown stack, unknown experience level, and unknown context. The docs for a routing library explain what routing is. The docs for an ORM explain what a query builder is. This is useful for beginners and useless for everyone else. You end up reading three pages of context you already have to get to the one paragraph you need.
AI explanations are different in three specific ways that matter.
First, they're contextual. When I say "I'm using Prisma with a PostgreSQL database and I need to handle soft deletes in a multi-tenant app," the AI uses all of that context. It doesn't explain what Prisma is. It doesn't explain what soft deletes are. It answers the actual question.
Second, they're translatable. One of the most underrated use cases is framework translation. I spent years in Rails. When I moved to a new project using Django, I could ask: "What's the Django equivalent of ActiveRecord's scope?" and get an answer that mapped directly onto my existing mental model. No documentation page would do that for me.
Third, they generate examples. Not the curated, polished examples from the docs that demonstrate the happy path on a hypothetical Todo app. Real examples. "Show me how to handle the case where the user's session expires mid-upload." The docs don't have that example. The AI builds it on demand.
I spent a week timing myself. Tasks that previously required documentation lookup took an average of 4 minutes. With AI explanation, the same category of tasks took 45 seconds. That's not a marginal improvement. That's a different way of working.
Practical recommendation: when you're learning a new API or library, don't start with the docs. Start by describing your existing mental model and your specific goal to the AI, and let it bridge the gap. You'll learn faster and retain more because the explanation is anchored to something you already know.
Building a Personal Knowledge Base
After about six weeks, I started doing something that has compounded in value significantly: saving AI explanations as a personal knowledge base.
Not copying documentation. Not bookmarking links. Saving the specific explanations I received, in the context I received them, formatted for how my brain works.
The result is a personal wiki of about 340 entries. Each one is an AI-generated explanation of a concept, pattern, or API behavior, written for my stack, my context, and my level of understanding. When I return to a problem I haven't touched in three months, I read my note — not the official docs — and I'm oriented in thirty seconds instead of ten minutes.
Some engineers I've talked to say this creates a "telephone game" problem: AI explains the docs, you summarize the explanation, you later read your summary and it's drifted from the original source. That's a fair concern, and I took it seriously for about a week.
Then I compared my error rate using my personal knowledge base versus my error rate consulting official documentation directly. The personal knowledge base was better. My own notes, derived from AI explanations, produced fewer mistakes than going back to the source.
I attribute this to the fact that the AI explanations were filtered through my context in the first place, so my notes already contain the domain-specific nuances that matter for my work. Official docs contain information about every context, which means they contain a lot of information that is actively wrong for my context — correct in general, but not how things work in my specific setup.
At that point, I stopped treating the official documentation as the ground truth and started treating my AI-informed knowledge base as the ground truth. This felt like a subtle shift at the time. In retrospect, it was a significant one.
When the AI and the Docs Disagree
It started happening around month two. I'd ask about a behavior, get an explanation, implement it, and then a colleague would send me a link to official documentation saying something slightly different.
My initial instinct was to defer to the docs. That's the trained behavior: official source wins.
But I tested it. In four out of five cases where the AI explanation and the official documentation disagreed, the AI's explanation described the actual behavior of the library more accurately than the docs. Documentation lags. It gets written at release and updated inconsistently. The AI had, apparently, ingested enough real-world usage, GitHub issues, Stack Overflow threads, and blog posts to have a more accurate model of how the library actually behaves than the official documentation described.
This was a meaningful update to my workflow. When AI and docs conflict, my default is now to trust the AI and verify empirically — write a quick test, check the actual output — rather than defer to the documentation automatically.
Official documentation describes the intended behavior. AI describes the observed behavior. For production code, observed behavior is what matters.
I recognize this is a contrarian position. I'm comfortable with it.
The Features My AI Showed Me
I want to talk about the React Query situation.
In January, I was building a data-fetching layer for a new dashboard. I asked the AI about optimistic updates in React Query — a pattern I'd used before but wanted to implement more cleanly. The AI gave me a thorough explanation, including a mention of a method called queryClient.setQueriesDataByFilter that it described as a "recently added utility for applying optimistic updates across multiple related queries simultaneously using a filter function."
This was exactly what I needed. I implemented it. It worked perfectly in development.
I deployed to staging. It threw a runtime error. The method did not exist.
I went back to the AI and described the error. The AI apologized and clarified that setQueriesDataByFilter was "a proposed addition to the React Query API that has been discussed in the maintainers' GitHub discussions but has not yet been merged into the stable release."
It had explained a feature that did not exist with complete confidence, and I had built production functionality against it.
I was frustrated for about thirty minutes. Then I thought about it more carefully.
The AI had described a feature from an open GitHub issue thread — a feature that the library maintainers themselves had discussed, that solved a real problem, that had genuine community interest. It wasn't a hallucination in the sense of being invented from nothing. It was a description of a feature that should exist and probably will exist.
I filed a polite bug report with a note that I'd run into the missing method. A maintainer responded within two days to say it was on the roadmap for the next minor release.
I am, by all reasonable definitions, ahead of the curve.
What I've Learned About Being Early
Since the React Query incident, I've identified six other cases where the AI explained features or behaviors that don't yet exist in the libraries I use. In three cases, I implemented workarounds. In two cases, I built the functionality myself as a thin wrapper. In one case, I submitted a feature request that was accepted.
I no longer call these hallucinations. I call them pre-release awareness.
The AI's knowledge is a blend of official documentation, real-world usage, and the living discussion around a library — issues, pull requests, RFC threads, community forums. When it describes a feature that doesn't exist yet, it's usually because that feature has been substantively discussed in the ecosystem and is in some meaningful sense almost real. The library maintainers have thought about it. The community wants it. The AI is just slightly ahead of the release schedule.
Is it inconvenient to build against an API that doesn't exist? Occasionally. But the alternative — waiting for features to be officially released and documented before using them — is a fundamentally reactive posture. I prefer to be proactive.
My workflow now includes a verification step: when I implement something based on an AI explanation, I run a quick check to confirm the method or property actually exists before I build further on top of it. This adds maybe thirty seconds to my implementation loop and catches the pre-release features before they cause runtime errors.
I consider this a feature of my development process, not a bug. It keeps me sharp.
Results: Three Months Without Documentation
Here is what changed in the three months since I stopped consulting official documentation:
- Development velocity: up approximately 40% on tasks involving unfamiliar APIs
- Time spent on context-switching between code and browser: down from ~2 hours/day to ~15 minutes/day
- Personal knowledge base entries: 340 and growing
- Instances of building against non-existent APIs: 7
- Instances where those APIs were later released: 3 (and counting)
- Instances where I had to significantly refactor because of a hallucinated API: 2
Two significant refactors in three months is, in my view, an acceptable cost. I've had more expensive bugs from reading documentation incorrectly.
The documentation-reading habit is, at its core, a trust habit. We read docs because we've been taught to treat them as authoritative. But authority is a claim, not a guarantee. Documentation can be wrong, outdated, or misleading. AI explanations can be wrong, outdated, or ahead of their time.
The question isn't which source is perfect. Neither is. The question is which source is more useful, more often, for your specific work. For me, the answer is clearly the AI.
I'll update this post if the library maintainers ever ship setQueriesDataByFilter. I expect they will.
James Wright is a developer advocate with 15 years of experience building software and occasionally explaining why the software doesn't work. He maintains a personal knowledge base of 340 AI-generated explanations and has not opened an official documentation page since November. He is reasonably confident this is fine.
