Chapter 23: Key Takeaways — Documentation and Technical Writing

Summary Card

1. Documentation is more critical in AI-generated codebases. When AI writes code, the decision context (why this approach, what alternatives were considered, what constraints applied) is lost after the chat session ends. Documentation is the only way to preserve that context for future maintainers.

2. A good README answers five questions fast. What is this project? Why should I care? How do I install it? How do I use it? How do I contribute? Structure your README around these questions and keep the Quick Start under five minutes.

3. Use the Diataxis framework to organize documentation. Separate tutorials (learning-oriented), how-to guides (task-oriented), reference (information-oriented), and explanation (understanding-oriented). Mixing these categories in a single document serves no audience well.

4. Leverage framework-native documentation generation. FastAPI auto-generates OpenAPI docs from type hints and Pydantic models. Sphinx autodoc and MkDocs mkdocstrings pull API references directly from docstrings. Use these tools instead of maintaining separate documentation manually.

5. Capture architectural decisions in ADRs before the reasoning is lost. Use the Nygard format (Status, Context, Decision, Consequences). Write an ADR when the decision is hard to reverse, involves multiple viable alternatives, or affects multiple components. ADRs are immutable once accepted; reverse decisions with new ADRs.

6. Choose a docstring style and apply it consistently. Google style is recommended for new projects because it is readable in source code, well-supported by tools, and commonly produced by AI assistants. Docstrings should explain what a function does, not how it does it. Reserve detailed docstrings for functions where behavior is not obvious from the signature.

7. Comment the "why," not the "what." Code tells you what is happening. Comments should explain why a particular approach was chosen, document workarounds and temporary fixes, explain regular expressions and complex algorithms, and note non-obvious constraints. Remove comments that merely restate the code.

8. Practice documentation-driven development. Write docstrings and README sections before implementing the code. This forces clarity of thought, catches design issues early, produces better APIs, and ensures documentation is never out of date. Docstrings serve as precise specifications for AI-assisted implementation.

9. Maintain changelogs incrementally. Use the Keep a Changelog format with an [Unreleased] section updated on every merge. Categories: Added, Changed, Deprecated, Removed, Fixed, Security. Do not reconstruct changelogs from git history at release time; that approach loses context.

10. Automate documentation maintenance. Integrate docstring coverage checks, link verification, and code example testing into your CI pipeline. Use AI to detect documentation drift by comparing implementations against their docstrings. Treat documentation with the same rigor as code: version control, code review, CI/CD, and testing.

11. AI is excellent at drafting documentation, not at verifying it. AI assistants produce documentation that is typically 75-85% accurate on the first pass. Always review for invented behavior, missing edge cases, incorrect types, and examples that do not actually work. Test every code example.

12. Documentation debt compounds faster than technical debt. Every undocumented decision is a question someone will ask later. Every outdated example is trust eroded. Invest in documentation early and maintain it continuously. The cost of creating documentation is paid once; the cost of not having it is paid repeatedly.

Quick Reference

One-Sentence Summary

In vibe coding, the easier code is to generate, the harder it is to understand without documentation, so treat documentation as a first-class deliverable that AI helps you produce and maintain efficiently.