Yes, AI can generate docstrings, API references, module summaries, setup guides, diagrams, and runbooks from a codebase. The output is useful when it stays grounded in executable behavior. Documentation generated from names alone can sound convincing while missing side effects, constraints, and failure modes.
Match the document to its reader:
- Code reference: Explain public interfaces, parameters, return values, and errors.
- Developer guide: Cover setup, commands, architecture, and common change paths.
- Operational runbook: Describe symptoms, diagnostics, recovery steps, and escalation.
- Decision record: Capture why an approach was chosen and which alternatives were rejected.
Ask the agent to cite the files, tests, and configuration that support each section during drafting, then remove internal planning notes from the publishable copy. Compare claims with actual commands and behavior. Mark examples as examples, not guaranteed outputs. Add documentation checks to code review so changed interfaces trigger corresponding updates.
Auto-documentation should reduce discovery time, not create a second source of truth. Keep contracts and defaults in code or configuration, then generate explanations around them. Begin with one high-friction area that new contributors repeatedly ask about. If the resulting guide helps someone complete a real task, expand the same process to the rest of the repository.
Related reading: How to validate AI-generated code and How to review AI-generated code before merging.
