Complete Debugging Guide for AI-Generated Code

Debugging AI Code Is Different

Debugging code you didn't write is fundamentally harder than debugging your own code. You lack the mental model of why the code was structured a certain way, which makes it harder to form hypotheses about what's wrong. This is the primary debugging challenge with AI-generated code.

Step 1: Understand Before Debugging

Before trying to fix a bug, read the AI-generated code. Form a mental model of what each function does. Ask the AI to explain its implementation: "Walk me through this function line by line. Why did you choose this approach?"

Step 2: Reproduce Reliably

AI-generated bugs often appear in edge cases the model didn't consider. Create a minimal reproduction case that triggers the bug consistently. This is your debugging foundation — if you can't reproduce it, you can't systematically fix it.

Step 3: Check Common AI Failure Patterns

• Off-by-one errors: AI frequently miscounts array boundaries, loop limits, and string positions.
• Null/undefined handling: AI often assumes values exist when they might not.
• Async race conditions: AI-generated async code sometimes has timing bugs — operations that depend on each other running in unpredictable order.
• Type coercion bugs: In JavaScript especially, AI generates code that works for expected types but breaks with unexpected inputs.
• Hallucinated APIs: The AI used a function or method that doesn't exist in your library version.

Step 4: Use AI to Debug AI

Ironically, AI is excellent at debugging its own code. Paste the buggy code along with the error and your reproduction steps. The AI often identifies the issue immediately because it recognizes patterns in its own output.

Step 5: Add Tests Before Fixing

Before changing the buggy code, write a test that fails due to the bug. Fix the code until the test passes. This prevents regressions and documents the expected behavior.