A payments team runs Claude Code inside its CI pipeline to review every pull request on the settlement service, and the review prompt instructs it to flag any security concerns it finds. Over 260 pull requests, reviewers dismissed 41 percent of the posted comments as not actionable, most of them speculative remarks about code that handles no external input. The team must bring the false positive rate down without losing the genuine findings. Which change most effectively addresses the measured rate?
- ARaise the acceptance threshold so that findings the model reports at high self-assessed confidence are the ones posted to the pull request.
- BMove the review job to the Message Batches API so that a slower and more thorough pass can run against every open pull request overnight.
- CReplace the open instruction with a named list of qualifying finding categories, each carrying one qualifying and one disqualifying example, and post nothing when no category matches. Correct
- DAdd a second review pass with a separately trained classifier that scores each drafted comment for usefulness before that comment reaches the pull request.
Why A is wrong: Tempting because a confidence gate is quick to add and appears to filter weak output, but a self-reported score is not calibrated against the objective criteria for a real defect, so speculative remarks that the model is sure about survive the gate while quiet genuine findings are cut.
Why B is wrong: Tempting because batch processing lowers cost and allows a longer pass, but the Message Batches API is asynchronous with a processing window of up to 24 hours, which cannot serve a review that must appear on the pull request during the build, and thoroughness is not the problem being measured.
Why C is correct: Correct because the false positives come from an instruction that defines no boundary for what counts as a security concern, and explicit categories with a matched pair of examples give the model a testable membership decision plus a sanctioned empty outcome.
Why D is wrong: Tempting because a dedicated model sounds like a stronger filter than prompt text, but it adds training and maintenance cost and a second inference stage inside a build-time budget before the cheap prompt fix has been tried, and it inherits the same undefined notion of a security concern.