Pasting a stack trace and asking it to “fix it” produces surface-level patches. Debugging well with AI requires giving it the same context you'd give a senior teammate.
The fastest way to get a fix that doesn't fix anything is pasting only the error message with no context. A stack trace alone tells the model where the failure happened, not why. For effective debugging in Cursor, always include: the full stack trace, the file where it occurs, and the files that call that function or provide the data that failed.
Explicitly asking it to investigate the cause "instead of a generic fix" changes the response: it forces the model to reason about the data flow instead of simply wrapping the line in a defensive if that hides the symptom.
A typical AI failure pattern in debugging is proposing the most local fix possible (adding a null-check right where it blows up) instead of tracing why the data arrived null in the first place. To force root-cause analysis, explicitly ask for the backward trace:
This usually reveals the real problem: for example, that an external webhook sometimes sends a partial payload, and the null-check in the service only hides a missing input-validation problem in the controller.
Before accepting a fix, ask for a test that reproduces the reported bug. This has two benefits: it confirms the model correctly understood the cause (if the test doesn't fail with the old code, the theory is wrong) and it leaves a permanent regression test in place.
This order — failing test, then fix, then passing test — is more reliable than asking for a direct fix, because it quickly exposes when the AI misunderstood the problem.
For bugs that aren't deterministically reproducible (race conditions, database connection issues, timeouts), Cursor's chat can help generate hypotheses but can't "guess" the cause without data. The effective workflow is to ask it to generate targeted additional logging, deploy it, and then analyze the real logs with the AI:
Once you have real production logs, paste them back into the chat for analysis — the AI is far more effective analyzing real evidence than speculating about a hypothetical race condition.
Composer can run commands if you grant permission, enabling a tighter debugging loop: run the test, see it fail, adjust the code, run it again, without you having to copy and paste output manually.
Explicitly asking it to "show me each iteration before continuing" prevents the model from iterating several times without you seeing the intermediate changes — important for not losing track of what was changed and why.
Bugs that depend on external state the model can't observe (specific behavior of an infrastructure version, an intermittent network issue, corrupted production data that isn't in the repo) require you to supply the evidence — logs, metrics, traffic captures. AI in Cursor drastically speeds up the "I have a hypothesis, let's verify it in code" cycle, but it doesn't replace the instrumentation and observability that only you can wire up from the production system.
Carlos Montiel is an enterprise AI solutions architect. He implements LLMs, Agents, RAG, and orchestrators for companies across Guatemala and Latin America. Reach out for a consultation.
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