Best Practices
Learn essential best practices for version control, testing, debugging, and continuous learning.
Version Control (Low Effort, High Reward)
Version control prevents catastrophic loss when AI modifies your codebase. It's one of the simplest yet most important practices for AI-assisted development.
- Prevents catastrophic loss when AI modifies your codebase
- Allows you to revert to previous working versions
- Tracks changes and progress over time
- Facilitates collaboration with others
- Provides a safety net for experimentation
Manual vs. Automatic Version Control:
Manual version control is better than automatic checkpoints, as it forces you to keep track of progress, provides clear restore points, and avoids the confusion of thousands of automatic checkpoints.
Git Workflow for AI-Assisted Development
Create a feature branch
git checkout -b feature/ai-assisted-feature
Commit before AI modifications
git commit -m "Before AI changes: [description]"
Apply AI changes
Make changes with AI assistance
Review and commit AI changes
git commit -m "AI-assisted: [description]"
Merge when satisfied
git checkout main && git merge feature/ai-assisted-feature
Testing AI-Generated Code
AI-generated code requires thorough testing. While AI can produce functional code, it may contain subtle bugs or misunderstandings of requirements.
Unit tests are particularly important for AI-generated code because they verify that each piece functions as expected in isolation.
- Test edge cases that the AI might have missed
- Verify input validation and error handling
- Check for correct business logic implementation
- Ensure performance meets requirements
Example Unit Test Prompt:
Debugging AI-Generated Code
Debugging AI-generated code requires a systematic approach. AI can help identify and fix bugs, but you need to provide clear context and error information.
Understand the code before debugging
Ask the AI to explain how the code works before trying to fix it
Provide complete error messages
Include stack traces, error messages, and relevant logs
Isolate the problem
Create a minimal reproduction of the issue
Use debugging tools
Leverage breakpoints, logging, and debugging tools
Verify the fix
Test thoroughly after applying AI-suggested fixes
Effective Debugging Prompts
❌ Ineffective Prompt:
"My code doesn't work. Can you fix it?"
✅ Effective Prompt:
"I'm getting a TypeError when calling the processData function. Here's the error message: 'Cannot read property 'map' of undefined'. The error occurs on line 24 when processing the API response. Here's the relevant code and the API response structure..."
- Incorrect assumptions about data structures
- Missing error handling
- Incomplete implementation of requirements
- Inefficient algorithms or patterns
- Security vulnerabilities
- Compatibility issues with existing code
Continuous Learning
AI tools are constantly evolving, and so should your skills. Stay updated on the latest AI capabilities and best practices to maximize your productivity.
Learning from AI
- Ask AI to explain its code and reasoning
- Request alternative approaches to the same problem
- Use AI to learn new frameworks and technologies
- Ask for best practices and patterns
Example Learning Prompt:
Building Your Knowledge
- Document AI-generated solutions and your modifications
- Create a personal knowledge base of effective prompts
- Join communities focused on AI-assisted development
- Experiment with different AI tools and approaches
- Share your learnings with others
Pro Tip:
Keep a "prompt journal" where you document effective prompts for different tasks. This will help you build a personal library of prompts that work well for your specific needs.