How to Debug Code Effectively Using AI: Give Context, Not Just Questions

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A software developer has shared a practical guide on using AI tools like Claude more effectively during code debugging sessions. The core advice is to stop describing problems vaguely and instead provide concrete evidence such as error stack traces, relevant code snippets, heap snapshots, and recent git commits. The author illustrates this with a real example where a Node.js WebSocket server was leaking roughly 100KB of memory per connection, a problem that was solved in minutes after sharing a heap dump and specific cleanup code with the AI. Tools recommended for gathering this evidence include Chrome DevTools heap snapshots, clinic.js for flame graph profiling, Node's built-in inspector, and git diff logs. The key takeaway is that AI works best as a pair-programming aid when supplied with precise, reproducible context rather than open-ended questions.

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