Step 5: Best practices
When to use AI for READMEs, what to always write yourself, and how to keep documentation current.
When AI adds the most value
New projects with no documentation: AI gets you from zero to a complete draft in minutes. Even an imperfect first draft is better than a blank file.
Repos with good code quality: Well-named functions, docstrings, and clear entry points give AI more signal, leading to better output.
Standard project types: CLI tools, libraries, and web apps follow predictable README patterns. AI has seen thousands of examples and generates accurate structure.
Multilingual teams: AI drafts in any language. Use --sections description with a target-language instruction in the prompt to generate descriptions in French, Japanese, or Spanish.
What to always write yourself
Real installation verification: Generate the install commands, then run them in a clean environment. Fix anything that does not work. Publish only tested steps.
Accurate code examples: AI cannot run your code. Generated examples look plausible but may have wrong argument names, incorrect output, or missing imports. Test every example.
Security and authentication notes: If your project handles credentials, tokens, or sensitive data, write those warnings manually. AI may omit or understate security considerations.
Known limitations and bugs: AI has no knowledge of open issues or known edge cases. Add these manually.
Environment-specific instructions: Instructions for Windows, Docker, or unusual setups require human verification.
Signs the AI output needs more work
Watch for these patterns in generated content:
| Pattern | What it means |
|---|---|
| "This project is designed to..." (no specifics) | AI had insufficient context — add more code or a stub README |
pip install my-project for a local repo |
AI assumed PyPI distribution — fix to pip install -r requirements.txt |
| Example function calls that don't match your code | AI hallucinated — verify against actual source |
| Generic contributing text | Fine to keep, but consider linking to a real CONTRIBUTING.md |
Keeping the README current
A generated README goes stale as the project evolves. Two approaches:
Manual updates: Regenerate individual sections when significant changes ship. Use --sections to target only what changed.
Document in your workflow: Add README review to your PR checklist or release process. A five-minute review catches most drift.
Treat generation as a starting point: The best README is one the maintainer understands and keeps accurate — not one that reflects perfect AI output at a single point in time.
README quality checklist
Before publishing any generated README:
- Project name and description are accurate
- All installation steps tested in a clean environment
- Code examples run without errors
- Expected output shown matches actual output
- Security/credential notes added where relevant
- License type is correct
- Links (if any) resolve correctly
- Tone matches the project and its audience
Summary
You have learned how to:
- ✓ Analyze a code repository and understand what context AI needs
- ✓ Generate a complete README section by section
- ✓ Customize prompts to improve output for specific audiences
- ✓ Use a Jinja2 template to control README layout
- ✓ Identify what AI generates reliably vs. what requires human review
The README generator works best as a starting point, not a finished product. Combine AI's speed with your knowledge of the project for documentation that is both fast to produce and accurate to publish.