Debugging with AI

Debugging with AI transforms one of development's most time-consuming tasks. Instead of manually tracing execution paths and searching Stack Overflow, you can describe the problem and get targeted analysis.

How AI Helps Debug

Error Analysis:

• Parses stack traces and error messages
• Identifies the root cause, not just symptoms
• Explains what the error means in context

Code Review:

• Spots logical errors humans miss
• Identifies edge cases not handled
• Finds potential null/undefined issues

Fix Suggestions:

• Provides corrected code, not just descriptions
• Explains why the fix works
• Offers alternative approaches

Effective AI Debugging Prompts

Instead of: "Fix this bug"

Try:

• "What's causing this error? [paste stack trace]"
• "Walk through this function step by step and identify where it might fail"
• "Why would this return undefined when I expect an array?"
• "Compare these two implementations and explain why one works and the other doesn't"

When AI Debugging Shines

• Unfamiliar error messages
• Complex async/timing issues
• Logic errors in dense code
• Understanding code you didn't write

Limitations

AI can miss bugs that require:

• Running the actual code
• Understanding external system state
• Domain-specific business logic knowledge

Use AI debugging as a powerful first step, then verify fixes with actual testing.