AI debugging —

AI debugging has become incredibly powerful. Instead of guessing which line is wrong, you can now describe the error and let the AI analyze your code. Here's how to use it effectively.

How It Works

AI debugging works by analyzing:

1. Error messages — Stack traces, exceptions, return values
2. Code context — Related files, function signatures, imports
3. Test failures — Output, assertions, expected vs actual
4. Recent changes — Git diff, modified lines

Effective Debugging Prompts

❌ Bad: "TypeError: Cannot read property 'map' of undefined"

✅ Good: "I'm building a user list component. The API returns an array of user objects, but the frontend crashes when trying to map over it. Here's the code and error. What's wrong?"

✅ Better: Provide full context:
Error: Database connection timeout
Context:
- Using Prisma ORM
- Database is running on localhost:5432
- Connection string: postgresql://user:pass@localhost:5432/db
- This just started happening after upgrading Prisma to v5

Here's the connection code and error trace."

Common Debugging Patterns

Best Practices

1. Be specific — Include error messages, stack traces, relevant code
2. Provide context — File structure, related functions, recent changes
3. Test the fix — AI might miss edge cases. Always test.
4. Iterate — If the first fix doesn't work, ask for alternatives.
5. Review before committing — AI makes mistakes. Always review.

Tools

The Bottom Line

AI debugging is now a must-have skill. It's 3-5x faster than manual debugging and catches 80% of bugs. Use it as a pair programmer — not a replacement for understanding your code.