Option A separates competing objectives so each review call can devote its attention and examples to a coherent concern. Anthropic’s Building Effective AI Agents describes sectioning as a parallelization pattern in which independent aspects are handled by separate model calls and then aggregated. It specifically notes that models generally perform better on complex tasks when each consideration receives focused attention. The evaluation results demonstrate prompt interference: optimizing business-logic detection reduces API-design recall.

A security and API-design reviewer can use examples, terminology, and evidence criteria appropriate to interfaces and vulnerabilities. A business-logic reviewer can focus on state transitions, arithmetic boundaries, invariants, and domain-specific edge cases. Their structured findings can then be deduplicated and ranked before posting. Option B may improve only the weak category and can overfit to known quiz-scoring failures. Option C assumes that model capacity alone removes prompt interference without evaluation evidence. Option D adds substantial unrelated context and does not isolate the competing review objectives. The split prompts should be evaluated independently and together so consolidation does not reintroduce omissions or duplicate findings.

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