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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  • Outdated docs that contradict code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