When AI Writes Bugs: A Debugging Guide

The Problem with AI-Generated Bugs

AI code generation tools produce code that looks correct at first glance but often contains subtle issues that only surface in production.

Common Categories

1. Hallucinated APIs

The most frequent issue is calls to APIs or methods that do not exist.

2. Stale Pattern Matching

AI models trained on older codebases reproduce deprecated patterns.

3. Silent Data Loss

AI-generated data transformations sometimes drop fields or silently coerce types.

A Debugging Framework

1. Reproduce — Isolate the AI-generated code in a minimal test case
2. Categorize — Identify which pattern above the bug matches
3. Fix locally — Patch the specific issue
4. Fix systemically — Update your prompting strategy or add a lint rule