← Bitkernel Interview Insights
Picked the answer about providing error messages, relevant code snippets, and the gap between expected vs actual behavior.
Emphasize that effective prompting for debugging is iterative and context-rich: start by providing the exact error, relevant code, and expected behavior, then refine based on the AI's response. Highlight the importance of verifying suggestions and using the AI as a collaborative tool rather than an oracle.
Pro tip: Show maturity by mentioning that you always ask the AI to explain its reasoning and suggest multiple hypotheses, which helps you learn and avoid blindly applying fixes. Also, note that you keep prompts focused and avoid sharing sensitive or proprietary code without proper safeguards.
Include the exact error message, stack trace, relevant code snippets, and a clear description of expected vs. actual behavior. This reduces ambiguity and helps the AI pinpoint the issue.
Request the AI to list possible root causes and explain its reasoning for each. This encourages a systematic approach and helps you evaluate suggestions critically.
If the initial response isn't helpful, refine your prompt by adding more details, narrowing the scope, or asking follow-up questions. Treat it as a dialogue.
Always validate AI-generated fixes in your local environment and consider edge cases. Don't assume correctness; use tests to confirm.
After resolving the issue, review what worked and what didn't in your prompting. This improves future interactions and your own debugging skills.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.