ai-dev-guidance

Best Practices for AI-Assisted Coding

This section provides guidelines and patterns for effective collaboration with AI coding assistants. These best practices will help you maintain code quality, manage complexity, and implement robust solutions when working with AI tools. If you find a best practice that works for you, please consider starting a discussion or starting a pull request to add it to this guide.

Available Guides

Managing Code Complexity

A comprehensive guide to maintaining optimal code complexity when working with AI coding assistants:

• Understanding cyclomatic complexity and its impact
• Guidelines for optimal code structure
• Effective prompting techniques for AI assistants
• Language-specific tools for measuring complexity
• CI/CD integration for automated complexity checks

Factory Pattern Implementation

A detailed guide to implementing the Factory Pattern for MCP servers with REST API integration:

• Core design principles for entity-centric organization
• Implementation components including abstract base classes
• Schema definition and validation
• REST API integration strategies
• Benefits of the factory-based architecture

AI-Supportive Developer Tooling

A comprehensive guide to designing and implementing developer tools optimized for AI coding agents:

• Core principles of AI-native tooling (token efficiency, deterministic environments)
• Implementation patterns including log insulation and domain knowledge indexing
• Project-specific tooling examples with code implementations
• Strategies for implementing .clinerules for AI agent guidance
• Scaling approaches for projects of different sizes and complexities

General Principles

When working with AI coding assistants, keep these principles in mind:

1. Maintain control over architecture decisions
   • Use AI for implementation details, not high-level design
   • Validate architectural suggestions against established patterns
2. Verify generated code quality
   • Review all AI-generated code for complexity issues
   • Apply consistent standards to both human and AI-written code
3. Incremental adoption
   • Integrate smaller, well-understood chunks of AI-generated code
   • Build up complexity gradually rather than all at once
4. Continuous learning
   • Document successful patterns for future reference
   • Share effective prompting techniques with your team
5. Balance automation with oversight
   • Automate routine coding tasks with AI
   • Maintain human oversight for critical components