AI Tools for Code Documentation: Docstrings, READMEs, and Changelogs

Documentation is the first thing teams promise to keep up to date and the first thing that falls behind once a deadline looms. AI tools have changed the economics of this problem — writing a docstring or changelog entry now costs seconds instead of minutes — but cheap generation doesn't automatically mean accurate documentation.

Docstrings: the easy win

Generating a docstring from an existing function is one of the safest uses of AI in a codebase, because the assistant has ground truth to work from: the function signature, its body, and how it's called elsewhere. Tools built into Copilot, Cursor, and most modern IDEs handle this well for:

• Parameter and return type descriptions
• Basic behavior summaries for straightforward functions
• Flagging missing edge-case handling worth documenting (e.g. "does not handle null input")

Where it gets shakier is intent — why a function exists, why it does something a non-obvious way, what tradeoff was made. AI can only guess at intent from code; it wasn't in the meeting where the decision was made. For those cases, generate the mechanical part and add a one-line human comment on the "why."

READMEs need more human framing than docstrings

A README's job is persuasion and orientation, not just description — it has to tell a new developer why the project exists and where to start. AI tools generate serviceable structure (installation steps inferred from a package.json, usage examples inferred from tests) but tend to produce generic-sounding prose about the project's purpose unless you feed them a clear brief. The fix is simple: write two or three sentences yourself about what the project is and why it matters, then let the assistant fill in the mechanical sections around that anchor.

Changelogs: generation quality depends heavily on input

Teams that adopt Conventional Commits get noticeably better AI-generated changelogs, because the structure removes the guesswork. Without that discipline, AI-generated changelogs tend toward vague entries like "various improvements" — technically not wrong, but useless to a user trying to understand what changed.

The drift problem

The core risk with any AI-documented codebase isn't the initial generation — it's that documentation silently drifts from code as the code changes and nobody regenerates the docs. A few practices reduce this:

• Run doc generation as part of CI on merge, not as a manual occasional task
• Treat documentation diffs as part of code review, not a separate pass
• Prefer tools that regenerate from source rather than ones that let a stale doc sit next to updated code indefinitely
• Spot-check AI-generated docs against the actual function behavior periodically — assistants can misdescribe edge cases they didn't fully trace

Where this fits in a broader workflow

Documentation generation pairs naturally with the debugging and review habits covered in our AI debugging workflow piece — both rely on treating AI output as a draft that needs verification against the actual code, not a finished artifact. For teams comparing which coding assistant handles this best across an entire workflow, our AI coding assistants hub has the full comparison set.

The bottom line

AI has removed the excuse of "documentation takes too long," which is genuinely valuable — most technical debt in this area was a time problem, not a willingness problem. It hasn't removed the need for someone to check that the generated text matches reality, especially for the "why" that no assistant can infer from code alone.