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.

Why Version Control is Essential
  • 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

1

Create a feature branch

git checkout -b feature/ai-assisted-feature

2

Commit before AI modifications

git commit -m "Before AI changes: [description]"

3

Apply AI changes

Make changes with AI assistance

4

Review and commit AI changes

git commit -m "AI-assisted: [description]"

5

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 Testing AI-Generated Code
Test individual functions and components

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:

"Generate unit tests for this user authentication function. Include tests for valid credentials, invalid credentials, account lockout after multiple failures, and password complexity validation."

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.

Effective Debugging Strategies
1

Understand the code before debugging

Ask the AI to explain how the code works before trying to fix it

2

Provide complete error messages

Include stack traces, error messages, and relevant logs

3

Isolate the problem

Create a minimal reproduction of the issue

4

Use debugging tools

Leverage breakpoints, logging, and debugging tools

5

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..."

Common Issues in AI-Generated Code
  • 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 Strategies
How to continuously improve your AI-assisted development skills

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:

"Can you explain how this React hook works? What are the potential pitfalls and best practices when using it? Can you show me alternative approaches to solve the same problem?"

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.