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You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline.

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 review generates many findings per pull request, but developer feedback shows that approximately half are dismissed as “not worth addressing.” Analysis reveals that these findings are often technically accurate but involve minor style preferences or patterns that are acceptable in the project.

Before adding infrastructure complexity, what prompt-design change would most effectively reduce dismissals while maintaining detection of genuine issues?

A.

Add a secondary classification model that filters findings according to predicted developer acceptance.

B.

Ask Claude to rate each finding’s confidence from 1 to 10 and include only findings rated 8 or higher.

C.

Define explicit reporting criteria that distinguish reportable bugs and security issues from minor style preferences and accepted local patterns.

D.

Add the instruction: “Only report findings you are highly confident are genuine problems.”

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