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.

After deploying automated code review, developers report that approximately 35% of flagged findings are false positives falling into consistent patterns: style suggestions contradicting team conventions, security warnings for patterns safe in your deployment context, and performance suggestions that would degrade your specific use case. You want to reduce false positives while maintaining the ability to catch genuine issues.

Which approach best enables the model to generalize its judgment to novel code patterns it hasn't seen before?