How AI Is Reshaping Documentation

AI-first workflows are killing the old way we write and read documentation

There’s a cliché in software development: “As soon as you write documentation, it’s outdated.” I think it’s one of the most important truths about how modern codebases actually work. Documentation goes stale fast and when it does, it doesn’t just become less helpful, it can actively get in the way.

Lately I’ve stopped relying on static documentation completely. I’ve found a better, faster, and more accurate way to get context: I go straight to the code and let AI do the reading.

My new workflow

When I need to understand a service or figure out how to interface with an internal API or external package, I don’t start by reading the documentation — I clone the repo.

I’m using Cursor  for most of this. I drop the codebase into the workspace and prompt the AI with whatever I need:

• “Write a client for this API based on the implementation.”
• “Use this package to do this thing.”
• “How does the data flow through this stack?”

Instead of relying on stale, incomplete, or overly abstract docs, I’m getting answers pulled directly from real code. And because the AI is interpreting the actual source, the responses are grounded, contextual, and up-to-date.

Why static docs break down

This shift started when I noticed that the documentation I was referencing — especially in older or poorly maintained codebases — was often just plain wrong. And not in an obvious way. I would follow the docs, hit a confusing edge case, and only then realize that something had changed months ago and nobody updated the docs.

Worse, when I fed that documentation to AI coding agents as context, it made things worse. The models would hallucinate, misinterpret, or steer me in the wrong direction. But when I removed the docs and pointed the AI directly at the source code, the quality of answers improved drastically.

The truth is, code doesn’t lie. It doesn’t drift (unless you let it). And now we have tools that can parse it, explain it, and give us instant access to the truth without needing to write a separate layer of interpretation.

What happens to documentation now?

I think we’re going to see a shift toward documentation that lives inside the codebase and is generated by AI, for AI. Tools like Cursor will help developers bake documentation directly into the codebase as markdown files or READMEs. Contextual overlays and summaries you can query live as you read and write code.

Since this new form of documentation lives inside the codebase, it can be written and updated by AI automatically — staying fresh as the code evolves.

Perhaps external documentation sites will evolve into interactive chat interfaces. You won’t be reading through ten pages of outdated setup instructions — you’ll be asking pointed questions directly to the code and the AI will respond with an accurate, up-to-date summary, links to relevant functions, and maybe even a generated diagram of the data flow. No hunting. No guessing.

This is huge for small teams and open source

Where I see this becoming especially powerful is in places where documentation has traditionally been the weakest; small teams, legacy codebases, and open source.

Small teams often don’t have the bandwidth to write and maintain proper docs. In open source, the original maintainer might be long gone. And in legacy systems, the docs might be so dense or outdated that they’re functionally useless.

But now you don’t need pristine documentation to get up to speed. If the code still works, you can understand it — with help from an AI interpreter. Even if it doesn’t work, you can quickly find out what’s wrong and fix it or work around it. That levels the playing field. It lets small, scrappy teams move faster without the overhead. And it gives open source contributors a way to explore unfamiliar projects without spending hours reverse engineering intent.

Documentation doesn’t disappear, it becomes on-demand.

What about non-engineers?

This shift will have ripple effects beyond engineering. Product managers, QA, designers — anyone who needs to understand how a system works — will benefit from AI-native docs.

They won’t need to read through the source or ping a dev for a walkthrough. They’ll be able to ask a question like:

“What happens when a user resets their password?”

And get an accurate, scoped, and understandable answer — along with references to the actual flow in code. With enough context, AI can even direct non-technical users to the right owning team for a given issue or to a dashboard for a given metric.

Final thoughts

Documentation isn’t going away. But the way we write, read, and interact with it is changing fast. In the age of AI, the most useful documentation is live, contextual, and embedded in the code itself.

It’s not something you write once and forget — it’s something you query, like a teammate who always knows what’s going on.

And for developers working across complex systems, small teams building fast, or contributors diving into legacy projects — that’s a game-changer.