Question

You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives. Your automated code review is missing genuine bugs in pull requests. Investigation reveals that your review prompt includes the instruction: ''Only flag critical issues that would definitely cause production failures. Ignore minor concerns and anything you are uncertain about.'' Developers confirm that some missed bugs are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with each finding tagged with metadata, and actionable. Which prompt change both removes the cause of the suppressed findings and preserves structured, tagged output for downstream filtering?

Accepted Answer

Instruct the model to report all findings with confidence and severity tags, deferring filtering to a downstream step.

Answer

Add a second review pass that rereads the diff using the same prompt, looking for anything the first pass may have missed.

Answer

Remove all severity-related instructions from the prompt and let the model use its default judgment about what to report.

Answer

Enable extended thinking and instruct the model to reason step by step about every code change before producing its review.

You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews…

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