Pair programming—where two developers work together on the same code—has been a valued practice in software development for decades. With the rise of AI coding agents, this practice has evolved into a human-AI collaboration that brings unique benefits and challenges. This technical guide explores best practices for effective pair programming with AI assistants, helping developers maximize productivity while maintaining code quality. (View Highlight)
Understanding AI-assisted pair programming
Pair programming with AI assistants represents a fundamental shift in how developers interact with their tools. Unlike traditional pair programming between two human developers, AI-assisted pair programming pairs a human developer with an AI coding agent that can generate, explain, and refine code. (View Highlight)
Key differences from traditional pair programming:
• Always available: AI assistants don’t need breaks, schedule synchronization, or rest periods
• Knowledge scope: AI assistants have broader language and framework knowledge but lack domain-specific understanding
• Interaction patterns: Communication happens through text/voice rather than natural human interaction
• Learning dynamics: The AI doesn’t learn from your codebase over time (without specific integration) (View Highlight)
Best practices for AI-assisted coding1. Establish clear roles and responsibilities
Effective pair programming with AI assistants requires clear role definition:
• Navigator Role (Human): Direct the overall development strategy, make architectural decisions, and review AI-generated code
• Driver Role (AI): Generate code implementations, suggest refactoring opportunities, and explain complex algorithms (View Highlight)
Provide contextual information
AI coding agents sometimes lack the full context of your project. To maximize effectiveness:
• Share relevant parts of your codebase when requesting assistance
• Explain project architecture and design decisions
• Specify coding standards and patterns to follow
• Define any constraints or requirements clearly (View Highlight)
Iterative development and refinement
Rather than expecting perfect code immediately:
• Start with a rough implementation and iterate
• Use the AI to generate alternative approaches
• Ask for explanations of complex sections
• Request refinements based on specific criteria
Example iterative process:
Critical code review
You should always review AI-generated code before implementation. While AI tools can produce functional code, they may:
• Generate security vulnerabilities
• Miss edge cases
• Introduce inefficient algorithms
• Make incorrect assumptions about your requirements
Tools like Diamond can significantly reduce the burden of reviewing AI-generated code. Diamond automates the identification of common errors, style inconsistencies, and inefficiencies in code produced by AI assistants. By leveraging Diamond, developers can focus on deeper logical checks and application-specific validation, thereby improving overall code quality.
Sample code review checklist:
• Verify security practices (input validation, proper authentication)
• Check edge case handling
• Evaluate performance characteristics
• Ensure readability and maintainability
• Validate against requirements (View Highlight)
Effective prompting techniques
Your prompts significantly impact code quality. Use these prompting strategies:
• Be specific and detailed about requirements
• Include examples of desired functionality
• Specify edge cases that need handling
• Reference existing patterns in your codebase
• Request explanations alongside code generation
Example of effective prompting: “Generate a React component that displays a paginated list of user transactions. It should:
• Fetch data from ‘/api/transactions’ with pagination parameters
• Display loading states during data fetching
• Handle and display error states
• Implement client-side filtering by transaction type
• Follow our component pattern where we separate data fetching from presentation
• Include test cases for the component” (View Highlight)
Knowledge transfer and learning
Developers can use AI pair programming as a learning opportunity:
• Ask the AI to explain complex algorithms or patterns
• Request alternative implementations to understand trade-offs
• Use the AI to learn new frameworks or languages
• Document insights gained from AI-assisted development (View Highlight)
