AI-Driven Development Maintenance
Summary of:
deepresearch/dev-workflow/ai-dev-maintenance.md
Key Points
- Spec-driven development: Write detailed specifications before coding; AI follows specs for consistent implementation
- Spec document structure: Include purpose, inputs/outputs, constraints, edge cases, and acceptance criteria
- Living documentation: Keep specs updated as requirements evolve; AI can help maintain documentation
- Test-first with AI: Write test specifications first, then have AI implement to pass tests
- Incremental complexity: Start with simple features, add complexity gradually with AI assistance
- Version control for specs: Track specification changes alongside code changes
- AI-assisted refactoring: Use AI to refactor code while maintaining spec compliance
- Validation loops: Regular human review of AI-generated code against specifications
Critical Insights
- Specifications are the contract - Clear specs reduce ambiguity and improve AI output quality; vague specs lead to inconsistent results
- Human-AI collaboration cycle - Humans define what, AI implements how, humans validate quality
- Documentation debt reduction - AI can help maintain documentation that typically falls behind code
Quick Reference
| Aspect | Recommendation |
|---|---|
| Spec format | Markdown with clear sections |
| Minimum spec content | Purpose, I/O, constraints, acceptance criteria |
| Update frequency | With every significant code change |
| Review cycle | Human review after each AI implementation |
| Refactoring approach | Update spec first, then refactor code |