Pattern: "Debug-only" collaboration with an LLM

How To
1

Story
Had a stubborn macOS tray menu bug in my pet project.
Tried the usual Cursor workflow: describe the bug in chat, attach screenshots, let the LLM patch code. Tested Claude-4 Sonnet, Gemini 2.5 Pro, GPT-4.1. Each one touched many files, said “fixed”, but the bug lived on (or the model broke something else).

New approach that finally worked:

  1. Tell the AI: “You can NOT change code, only add temporary debug logs.”
  2. Ask it to give me a ready-to-copy grep command for the app logs after every change.
  3. Run the app, reproduce the bug, copy the filtered logs back to the chat.
  4. Repeat.

Four or five iterations later we traced the issue to wrong cache invalidation in the data layer, not the UI. Once I knew that, the actual fix was tiny.

Takeaways
• Treat the LLM as a smart lab assistant, not an auto-coder.
• Real runtime data > code guesses.
• Short, structured feedback loops keep you in charge and stop wild refactors.
Hope this pattern is useful to someone here. I’m definitely adding it to my toolbox!
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Happy debugging!