Why documentation is the perfect AI task
Documentation rots because writing it is tedious and writing it again after every change is worse — so it gets skipped. This is exactly the kind of mechanical, context-bounded work LLMs excel at. Given the actual code, a model will produce accurate docstrings, README sections, and changelogs in seconds, turning the chore that always loses to deadlines into a step that costs nothing. The key is feeding it the real code and constraining it to describe only what it can see.
How the documentation workflows work
Docstrings and JSDoc. Paste the function and prompt: “Write a JSDoc comment for this function. Describe each parameter and the return value. Note thrown errors. Do not invent behaviour not present in the code.” Because the behaviour is fully determined by the code, the output is reliable. Do this per function and the model never has to guess.
README sections. Give the model the package.json (or equivalent), the main entry file, and a one-line description of the project, then ask for the standard sections: what it does, installation, quick-start usage, and configuration. Review for accuracy on commands, since those are the parts most worth getting right.
Changelogs. Pass the commit messages since the last release and ask the model to group them into Added / Changed / Fixed / Removed in user-facing language. Conventional commit prefixes make this almost mechanical.
Explaining legacy code. For an undocumented module nobody understands, ask “explain what this code does, step by step, and flag anything that looks like a bug or a side effect.” This is one of the highest-value uses — turning fear-inducing legacy into something a team can reason about.
Tips for accurate, maintainable docs
Keep each prompt scoped to one unit plus the signatures it depends on — small prompts are cheaper and far more accurate than dumping a whole repo. Constrain the model to describe only what is in the code; the most common failure is confident invention when it is asked to document something it cannot fully see. Wire generation into CI or a pre-commit hook so docs regenerate only for changed files and a human reviews the diff — this is what stops documentation from rotting after the first write. Finally, let AI handle the mechanical bulk but keep the why — architecture rationale, migration warnings, design trade-offs — in human hands, since those require judgement the model does not have.