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You built an LLM-powered code-review tool that analyzes pull requests and returns structured findings.

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You built an LLM-powered code-review tool that analyzes pull requests and returns structured findings. Each finding is a JSON object containing file_path, line_number, issue_category—such as security or style—and description. Developers can dismiss findings they consider unhelpful, and currently 35% of findings are dismissed. You want to analyze these dismissals to understand what the system is getting wrong and improve the prompts accordingly. What change to the output structure would best support this analysis?

A.

Add a model_confidence field from 0.0 to 1.0 and filter findings below a threshold calibrated against historical dismissal rates.

B.

Add a detected_pattern field recording the specific code construct that triggered the finding, such as single-letter loop variable.

C.

Expand the description field with more detailed explanations of why each issue matters and how it should be fixed.

D.

Remove the issue_category field and track dismissal rates only at the individual-finding level.

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