AI Documentation Generator

Built an AI-assisted documentation system using the Claude API with structured output parsing to auto-generate Markdown documentation across a multi-language codebase. Reduced the manual documentation effort and produced consistent, structured technical docs.

Designed a multi-language documentation pipeline that analyzes source files across the codebase. Each language has a tailored analysis prompt that understands the framework conventions for that ecosystem (ROS node structure, class hierarchies, component lifecycles, configuration formats).

Integrated with the Claude API using structured output parsing to generate consistent documentation following a standard Markdown template: module overview, public API reference, key dependencies, configuration parameters, and usage examples. The structured output ensures every generated doc follows the same shape regardless of source language. A post-processing step cross-references generated docs to add hyperlinks between related modules.

The system reduced documentation effort substantially — turning a multi-day manual process into a short review-and-refinement pass. Output is served via a Flask-based documentation server that renders Markdown with navigation, search, and cross-module linking — maintained as living documents that regenerate when the underlying code changes.