How to Document Your Codebase with AI [2026]

AI writes the documentation whose answer is already in the code. A guide to doc comments, API reference, architecture docs, and keeping them current.

Per Harald Borgen

Per Harald Borgen

— 10 min read
How to Document Your Codebase with AI [2026]

AI is good at the documentation whose answer is already sitting in the code, and unreliable at the documentation that is not. Doc comments, API reference, and summaries of existing behavior it produces faster and more consistently than you will. Intent, tradeoffs, and the reason you rejected the other approach it invents, because none of that is written down anywhere it can read. Underneath both halves is one uncomfortable fact: documentation rots for two reasons, and AI fixes one of them. This guide covers which layers to automate and how to keep the output from going stale.

Why Does Documentation Rot, and Which Half Does AI Fix?

Documentation rots because writing it is tedious and updating it is thankless. AI collapses the first cost to nearly nothing and leaves the second exactly where it was.

The cost of writing is paid once, by somebody who would rather be coding. It is unpleasant, which is why so much of it never gets paid at all.

The cost of keeping it true is paid every time the code changes, forever, by whoever happens to notice. Nobody has ever been promoted for paying it. This is the cost that kills documentation. The README in most repositories was not written badly. It was written once, accurately, and then the code moved.

An AI model changes the first number dramatically and does not touch the second, because generating a paragraph creates no mechanism that regenerates it when the function changes six weeks later.

So the obvious move backfires. Generate documentation for an entire repository and you have not reduced your documentation debt. You have increased the surface area that can go stale.

AI made documentation cheap to write. It left it exactly as expensive to keep true.

What Is Worth Documenting, and What Is Just Noise?

A doc earns its place when it tells a reader something the code does not already say. Everything else is noise, and noise carries a maintenance cost.

Most generated documentation fails that test:

/**
 * Gets a user.
 * @param {string} userId - The user ID.
 * @returns {User} The user.
 */
function getUser(userId: string): User

Every word of that was already on the line below it. In TypeScript it is redundant by convention too: the TSDoc standard writes @param name - description with no type, and TypeDoc does not support many JSDoc tags at all "because the TypeScript compiler can infer the same information directly from code."

The version that earns its place answers what the signature cannot say:

/**
 * Throws NotFoundError when the user was deleted. Deleted users are
 * never returned as null. Reads from the replica, so a user created
 * in the last few seconds may not be visible yet.
 */
function getUser(userId: string): User

Worth documenting, more or less always:

  • The failure mode. What throws, what returns empty, what silently does nothing.
  • The unit. Milliseconds or seconds, cents or dollars, inclusive or exclusive.
  • The non-local constraint. Rate limits, ordering requirements, call this before that.
  • The reason a workaround exists, so the next person does not helpfully delete it.
  • The thing every newcomer gets wrong.

Every doc comment you generate is a maintenance liability. Generating one for every function is a decision, not a default. Public surface first, internals only where they surprise. Scrimba's Introduction to Clean Code (Pro, 64 minutes, with Dylan C. Israel) works through this distinction alongside naming and function design. Pro is $24.50 per month billed annually, with regional and student discounts available, and free courses include certificates.

The Four Layers of Documentation, and Which Ones AI Can Carry

Documentation is not one thing, and its four layers relate to a model very differently.

Layer What it answers Is the answer in the code AI fit
README and getting started What is this, who is it for, how do I run it Partly. Build scripts and dependencies yes, audience and purpose no Good first draft, human on the framing
Architecture and why How the pieces fit, and why they are shaped this way The how, yes. The why, never Strong on structure, unreliable on intent
Runbooks What to do when it breaks at 3am Almost never Weak, and confidently wrong here is operationally expensive
API reference What each symbol takes, returns, and throws Entirely The strongest case, and the ecosystem already does most of it

The third column drives the fourth. AI fit tracks exactly one variable: whether the answer already exists in the code. Better models will not change that: the limit is not reasoning, it is the absence of information.

Which is the awkward part. The documentation people actually need is the why, and the why has the worst fit. Intent, tradeoffs, and rejected alternatives live in a design review, a chat thread, and somebody's memory. A model has access to none of it, and rather than say so it will write a paragraph that sounds like it knows.

How to Generate Doc Comments and an API Reference

Generate the doc comments with AI, then let your language's own documentation generator build the reference from them. Never have a model write the reference itself.

Language Convention and generator
JavaScript JSDoc
TypeScript TypeDoc, using TSDoc syntax
Python PEP 257 docstrings, built with Sphinx
C and C++ Doxygen
Rust rustdoc, via cargo doc
Go Go doc comments, read by go doc
HTTP APIs OpenAPI, currently at 3.2.0

The procedure:

  1. Match the convention already in the repository. Three comment styles is worse than none.
  2. Scope it to the public surface. Exported functions, published endpoints, anything another team calls.
  3. Build the reference locally and read the output, not the diff. Rendered pages expose padding a code review misses.
  4. Delete every comment that restates the signature. This will be a large fraction of the first pass.
  5. Commit the comments and the build step together, so the reference is reproducible.

For an HTTP API the artifact worth having is an OpenAPI description rather than prose, because it can be checked against the running service instead of trusted. If those endpoints are still being designed, Scrimba's guide to building REST APIs covers the decisions this step assumes you made.

Dedicated tools sit in this layer. Pricing in this section was checked against each vendor's own page in August 2026. DocuWriter.ai runs three generators over a connected repository, producing book-style documentation, an API reference in OpenAPI or Postman form, and UML diagrams, from $20 per month. Sourcery is now primarily an AI code review product at $15 per seat per month, though its editor extension keeps a Generate Docstrings recipe on every plan.

A caution for all of them: a model asked to document an existing function describes what the code does, including the bug. A doc comment that faithfully documents incorrect behavior is still wrong documentation.

How to Draft an Architecture Overview with a Repo-Aware Agent

A coding agent with repository access can map components and data flow across files, which is what an architecture overview needs.

Claude Code, Codex, and Cursor all read across a repository rather than a single open file. Cursor's documentation puts it usefully: its codebase search captures how components interact across the project, and it can compare a README against the current code to report what is stale.

The procedure that produces something usable:

  1. Point the agent at entry points, not the whole tree. Main, the router, the job queue, the migrations.
  2. Ask for a map, not an essay. Components, what talks to what, where data enters and leaves.
  3. Require a file path beside every claim. This makes the output reviewable at all.
  4. Verify those paths yourself before anything is committed.

One place here has a selfish incentive attached. Coding agents read a project instruction file at the repository root, CLAUDE.md for Claude Code and AGENTS.md for Codex, so an accurate architecture summary is documentation for humans and context for your next agent session at once. Stale docs make the agent worse, and you notice within a day. Claude Code writes the first version for you: /init analyzes the codebase and generates a CLAUDE.md with the build commands, test instructions, and conventions it finds. Codex has no documented equivalent.

Mintlify's documentation agent, on its paid plans, is worth copying in shape even if you never use it: research the repository, write, validate the result with a CLI check, then open a pull request rather than publish.

Scrimba Explain takes a different route to the same job. It is an MCP plugin: you ask your coding agent a question about the codebase, the agent researches it against the files and conversation it already has, and Explain returns a narrated video walkthrough instead of a wall of text. It works with Claude Code, Codex and ChatGPT, and any agent that supports MCP, and it is free during open beta.

The format has a real cost, worth knowing before you reach for it. Video is harder to diff, harder to search, and harder to keep current than text. There is no line-level diff of a narration, so a walkthrough documents the codebase as of the day it was made and gives no signal when that stops being true. Good for onboarding and for explaining one thing once. Poor as the canonical record. Scrimba's own FAQ adds the other caveat: like any AI tool it can make mistakes, so double-check anything important.

ADRs and the Part AI Cannot Supply

An Architecture Decision Record is a short note capturing one decision and its rationale, kept in the repository beside the code it explains.

The format comes from Michael Nygard's 2011 post, the piece that popularized the practice (adr.github.io). Five parts, in his order: Title, Context, Decision, Status, Consequences. Two details get dropped by almost everyone who adopts it. Context describes "the forces at play" in value-neutral language, and those forces are usually in tension. Consequences lists all of them, not only the ones that support the decision.

An agent helps with part of this: drafting Context from the diff and surrounding code, keeping the format consistent, and spotting decisions that were made and never written down.

It cannot supply the rest. The alternatives you considered, why you rejected them, the constraint that arrived from outside the repository, what you were worried about at the time: none of that exists in the code. Asked for it anyway, a model produces something plausible, which is worse than a blank. A blank invites a question. A confident paragraph does not.

Practical split: let the agent draft Context, and write Decision and Consequences yourself.

How to Keep Documentation Current in CI

Documentation stays current only when going stale breaks something. Put the check in CI, so a change that invalidates a doc fails the build.

This is the cost AI does not touch, and it decides whether any of the above was worth doing. Four gates, cheapest first:

  1. Require doc comments on the public surface with a linter, not a review convention. In Python that is Ruff's pydocstyle rules. In TypeScript it is TypeDoc's validation.notDocumented, worth knowing about because it is off by default.
  2. Treat documentation warnings as errors. sphinx-build -W and typedoc --treatWarningsAsErrors turn a broken cross-reference into a failed build rather than a page that ships wrong.
  3. Lint the prose. Vale checks style and terminology, and exits non-zero on error-level alerts so CI can fail on it.
  4. Check the API description against the running service, so an OpenAPI file cannot quietly describe an endpoint that no longer exists.

The strongest version couples documentation to code directly. Swimm's Auto-sync keeps the code snippets inside a doc current through CI, and when a change is significant enough its verification check fails and blocks the commit or pull request while the affected doc is flagged as potentially out of date. Whatever tool you use, that is the mechanic to steal.

The only documentation that stays current is documentation that fails a build when it goes stale. Everything else is a good intention with a checkbox in the pull request template.

One limit: none of these gates can tell you the prose is true, only that it exists, builds, and matches the shape of the code. All of it lives in the repository, so Git and GitHub fluency is the prerequisite.

How to Review AI-Written Documentation

An authoritative-sounding wrong doc is worse than no doc. Review generated documentation the way you would review a pull request from a stranger.

Missing documentation makes somebody ask a question. Wrong documentation makes them confident. Generated prose removes the usual warning signs: no typos, no half-finished sentences, nothing that looks rushed.

The checklist, all of it fast:

  • Every file path and symbol named in the doc exists. Search for it.
  • Every command in the doc has been run, by you, in that order.
  • Every version number was checked rather than recalled.
  • No configuration key, flag, or environment variable appears that you cannot find in the code.
  • No sentence states intent that the code cannot prove.

The last item catches the most damaging errors and is the one reviewers skip, because a sentence about why something was built a certain way reads like context rather than a claim.

The layers where AI is strongest are also the layers where this review is fastest, since every claim is checkable against the code. Same variable again.

Frequently Asked Questions

Can AI write documentation for my codebase?

Yes, for the layers whose answer already exists in the code. Doc comments, API reference, and summaries of current behavior are reliable. Intent, tradeoffs, and rejected alternatives are not, because that information was never in the repository for a model to read.

What should you not let AI document?

Anything that records a decision rather than a behavior. Runbooks, architecture rationale, and the alternatives you rejected all live outside the code. A model asked for them produces confident, plausible text, and a plausible wrong answer is harder to catch than a blank section.

Which AI tool is best for generating code documentation?

There is no single winner, because the layers differ. Repository-aware agents such as Claude Code, Codex, and Cursor suit architecture drafts. Documentation platforms suit publishing and review workflows. Your language's own generator, such as TypeDoc or Sphinx, should still build the API reference.

How do you stop AI-generated documentation from going out of date?

Put the check in CI. Require doc comments with a linter, treat documentation build warnings as errors, and couple docs to code so a change that invalidates a doc fails the build. Without a failing check, freshness depends on somebody volunteering.

Is video documentation worth it?

For onboarding and one-off explanation, often yes, because a narrated walkthrough carries structure that text has to work harder to convey. As the canonical record, no. Video cannot be diffed, is awkward to search, and gives no signal when it stops being accurate.

Key Takeaways

  • AI changes the cost of writing documentation, not the cost of keeping it true, and the second cost is the one that kills documentation.
  • AI fit tracks exactly one variable: whether the answer already exists in the code. Reference and doc comments qualify. Intent and rationale do not.
  • A generated doc comment that restates the function signature is noise, and noise carries a maintenance cost.
  • Generate the doc comments, then let your language's own generator build the reference from them.
  • An authoritative-sounding wrong doc is worse than no doc, so review generated documentation like a pull request from somebody who never ran the code.

Sources

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