After deploying automated code review, developers report that approximately 35% of flagged findings are false...

Option B teaches Claude the actual decision boundaries behind the team’s judgments. Contrasting examples can demonstrate why one apparent security pattern is acceptable within the organization’s deployment controls while a superficially similar implementation is genuinely unsafe. The same technique can distinguish established style conventions from deviations and intentional performance trade-offs from accidental inefficiencies.

Anthropic identifies relevant, diverse, and clearly structured examples as one of the most reliable ways to improve Claude’s accuracy and consistency. Its official prompting guidance recommends examples that closely mirror the production task while covering important variations and edge cases. This encourages generalization based on demonstrated criteria instead of memorization of prohibited wording.

Option A can suppress legitimate findings merely because they contain a selected keyword. Option C will become lengthy, difficult to maintain, and incapable of enumerating every future code variation. Option D is too vague and may reduce recall by discouraging Claude from reporting uncertain but genuine defects. Few-shot contrasts provide precise, maintainable behavioral guidance without imposing a blanket suppression policy.

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