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Claude Code Documentation Best Practices

Summary of: deepresearch/dev-workflow/claude-code-documentation-best-practices.md

Key Points

  • Documentation as context: Well-structured docs improve AI understanding and output quality
  • README.md essentials: Project purpose, setup instructions, architecture overview, key commands
  • API documentation: Clear endpoint descriptions, request/response schemas, error codes
  • Code comments strategy: Focus on "why" not "what"; avoid redundant comments that restate code
  • Architecture Decision Records (ADRs): Document significant technical decisions for AI and human context
  • Inline documentation: JSDoc/PHPDoc for functions, interfaces, and complex logic
  • Living documentation: Keep docs updated with code changes; stale docs are worse than no docs
  • AI-readable structure: Use clear headings, code blocks, and structured formats

Critical Insights

  1. Documentation serves dual purpose - Helps both humans and AI understand codebase; invest in quality documentation
  2. Comments should explain intent - "Why" comments are valuable; "what" comments are noise
  3. Structure matters for AI - Well-formatted markdown with clear sections improves AI comprehension

Quick Reference

Document TypePurposeKey Content
README.mdProject entry pointSetup, overview, quick start
CLAUDE.mdAI contextStandards, patterns, guidelines
ADRsDecision historyContext, decision, consequences
API docsIntegration guideEndpoints, schemas, examples
Inline docsCode contextFunction purpose, parameters, returns

Documentation Anti-Patterns

  • Commenting every line of code
  • Outdated docs that contradict code
  • Documentation in silos (not near code)
  • Missing "why" explanations
  • Over-documentation of obvious code
docs/
├── README.md # Project overview
├── ARCHITECTURE.md # System design
├── API.md # API reference
├── DEVELOPMENT.md # Dev setup guide
├── DEPLOYMENT.md # Deployment procedures
└── adr/ # Architecture Decision Records
├── 001-database-choice.md
└── 002-auth-strategy.md