Generate code documentation with AI and keep it true after the next commit
Docstrings and READMEs are mechanical. The business rules behind them are not.
In short
To generate code documentation with AI, run it as part of the change that touches the code, so the docs move when the code does.
- It handles docstrings, parameter and return descriptions, and starter READMEs reliably.
- It cannot infer why a rule exists. Domain reasoning still has to be written by a person.
- Documentation that drifts is worse than none, because people trust it.
Generate at the moment of change
The reason documentation rots is that writing it is a separate job from changing the code, so it loses every time. Generate code documentation with AI and that gap closes. You change a function, ask for its docstring in the same pass, and the description ships with the diff. Models are dependable on the mechanical layer, meaning parameter and return descriptions, thrown exceptions, usage examples, and a starter README for a new module. Where they fail is intent. A model reads what the code does and will confidently invent a reason for a rule that exists because of a regulator or a customer incident. So generate the mechanical eighty percent and write the rest, especially anything explaining why. Hold the output to a shape with structured output prompting, and review it the way you would any AI content. It is part of AI for engineering.
How to generate code documentation with AI
Document at change time, in your house format, then review the reasoning.
- 1
Document in the same pass as the change
Ask for the updated docstring when you edit the function. A separate documentation sprint never happens.
- 2
Name the format explicitly
'Google-style Python docstrings' or 'JSDoc with @throws.' Say which convention or you get a mix.
- 3
Give it the calling context
Paste the function plus one real call site. Usage examples improve sharply when it can see how the code is used.
- 4
Ask it to mark what it inferred
'Flag anything you guessed rather than read from the code.' That list is exactly what you need to rewrite.
- 5
Write the why yourself
Business rules, regulatory constraints, and the reason behind a strange branch. A model will invent a plausible reason.
- 6
Review it like code
Docs go through the same pull request. An unreviewed generated doc is a confident claim nobody checked.
Watch-outs
- Models invent rationale for rules they cannot explain. Treat every "because" in generated docs as unverified.
- Stale documentation is worse than none, because people act on it. Regenerate when the code changes.
- Generated examples may not compile. Run any usage snippet before it ships.
Common questions
How do I generate code documentation with AI?
Ask for the docstring in the same pass as the code change, name the convention you use, and paste a real call site so the examples are grounded. Then review it in the pull request like any other change, rewriting anything that explains why a rule exists.
What documentation does AI handle well?
The mechanical layer: parameter and return descriptions, thrown exceptions, type information, usage examples, and starter READMEs for a new module. These follow predictable patterns that a model reproduces consistently across a codebase.
What should a person still write?
Intent. Why a rule exists, which constraint a strange branch satisfies, and what a regulator or customer incident forced. A model reads what the code does and will produce a confident, plausible, wrong explanation for why.
How do I stop the docs going stale?
Tie generation to the change rather than the calendar. If updating a docstring happens in the same commit as the function edit, the documentation stays in step; if it is a separate task, it falls behind within a release or two.
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