Introduction
For many developers, writing documentation is the least exciting part of a project. Yet, good docs are essential for onboarding new contributors, reducing support requests, and ensuring long-term maintainability.
Thanks to AI tools, generating and maintaining documentation is becoming much easier. From API references to project wikis, AI can handle the heavy lifting while developers focus on building features.
In this post, we’ll explore how to use AI for automating documentation, including generating API docs and project wikis, along with best practices and limitations.
Why Automate Documentation?
Manual documentation has a few common problems:
- It takes significant developer time
- Often becomes outdated as code changes
- Lacks consistency in style and structure
- Gets deprioritized compared to feature work
Automating parts of the process ensures documentation stays accurate and up to date with minimal effort.
Using AI to Generate API Documentation
Code Annotations and Comments
AI can scan your codebase and turn inline comments into structured API documentation. For example, a @param comment in a function can be expanded into a full description.
Auto-Generated Reference Docs
Tools like Swagger, Redocly, or Docusaurus can be combined with AI to automatically generate clean, readable API references from code annotations.
Summarizing Endpoints
LLMs can create human-readable summaries of endpoints, including example requests and responses, making your docs more developer-friendly.
Building Project Wikis with AI
Beyond APIs, AI can also help maintain project wikis or knowledge bases.
- README files: AI can draft README.md files with setup instructions and usage examples.
- Architecture docs: Based on directory structures and comments, AI can outline how modules interact.
- How-to guides: Developers can generate step-by-step tutorials from code snippets or commit history.
This helps new team members ramp up quickly without overwhelming senior engineers.
Best Practices for AI-Generated Documentation
- Always review AI outputs: Use them as drafts, not final versions.
- Combine with CI/CD: Regenerate and update docs automatically during builds.
- Keep human context: Business logic and unique decisions often need manual explanation.
- Version your docs: Ensure generated docs match the version of the code being released.
Limitations to Consider
- AI may produce generic explanations that miss project-specific details
- Documentation can become too verbose without careful prompts
- Sensitive information (like API keys or configs) may accidentally get included if not filtered
Conclusion
Automating documentation with AI saves time, reduces technical debt, and makes projects easier to maintain. From generating API references to building project wikis, AI tools can provide strong first drafts that developers refine and publish.
The best approach is hybrid: let AI generate the structure and bulk content, then add human expertise for clarity and accuracy.
If you’re already streamlining your development with automation, you may also like our post on CI/CD using GitHub Actions, Firebase Hosting & Docker. For a broader look at AI-powered documentation, check out OpenAI’s guide on technical writing with LLMs.
