What to do
Use AI debugging in an evidence loop: reproduce the failure, collect the smallest useful trace, rank hypotheses, test one variable at a time, make the smallest fix, and add a regression test.
Build a reliable reproduction
Record the exact input, environment, version, expected result, actual result, and frequency. Reduce the case before asking for a fix. Without reproduction, the model may explain a plausible but unrelated problem.
Ask for hypotheses, not certainty
Request a short ranked list and the observation that would confirm or reject each item. Run the cheapest discriminating check first. Feed back actual output rather than paraphrasing it.
Close the loop
Apply the narrowest change that explains the evidence. Run targeted and broader tests, consider adjacent failure modes, and preserve a regression test. Document the root cause separately from the symptom.
- Do not paste credentials or customer data.
- Treat commands as proposals before execution.
- Verify framework and version assumptions.
- Revert experiments that do not support the diagnosis.
Practical checklist
Continue researching
This guide is an editorial framework, not a product endorsement. Recheck vendor documentation and your organization's requirements before making a purchasing or security decision.