AI Debugging

Debugging begins with evidence of a failure. An AI coding assistant can trace related code, suggest hypotheses, and prepare a fix, but a plausible explanation is not proof of the cause. Give it a reproducible example, the expected behavior, and relevant logs or test output. Work in a controlled checkout so proposed changes remain reviewable. This workflow focuses on diagnosing and fixing an observed defect; code generation starts from a requested feature and remains a separate use case.

A practical workflow

1. Reproduce the failure and reduce it to the smallest useful example.
2. Collect the relevant stack trace, inputs, environment, and recent changes.
3. Ask the assistant to trace the failing path and state a testable explanation.
4. Prepare a focused change and a regression check that fails on the old behavior.
5. Review the diff, run the appropriate checks, and confirm the original reproduction now succeeds.

What to compare

• Distinguish a symptom workaround from a change that addresses the failure's cause.
• Check generated commands and access to sensitive environment data.
• Avoid unrelated refactors that make the defect harder to review.
• Compare tools on evidence use, patch quality, and successful reproduction.

Choose tools for this workflow

Use the shortlist below to compare approaches. Test the same representative task in each product and inspect the output before expanding the workflow. Review the product profile for its scope and plan-specific limits.

Explore tools for this workflow.