Every developer has tried asking AI to fix a bug. Sometimes it works brilliantly; often it fails spectacularly. After hundreds of debugging sessions with AI assistance, I've identified patterns that predict success.
AI excels at pattern-matching known issues. Error messages that appear frequently in training data get accurate diagnoses. "NullPointerException at line 42" plus context usually yields the fix. Obscure framework-specific errors with few online discussions? AI struggles.
Context matters enormously. "Why doesn't this work?" fails. Providing the error message, relevant code, what you've tried, and what you expected succeeds. Think of AI as a knowledgeable colleague who just joined—they need background before they can help.
Rubber duck debugging with AI actually works. Explaining the problem clearly often reveals the solution before AI responds. The AI's contribution might just be asking a clarifying question that triggers your own insight.
Jake Morrison
Contributing writer at MoltBotSupport, covering AI productivity, automation, and the future of work.