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

Option B creates the granular error taxonomy needed to understand recurring false-positive patterns. The existing issue_category field is too broad: a high dismissal rate for style does not reveal whether developers object to single-letter variables, line-length warnings, naming conventions, or something else. Recording the detected construct allows dismissal rates to be grouped by trigger and connected to targeted prompt revisions or project-specific examples.

Anthropic’s evaluation guidance recommends measurable, task-specific criteria and evaluation cases that reflect real production behavior and edge cases. Its structured-output documentation supports schema-constrained, parseable records suitable for this type of downstream analysis.

Option A can help rank findings, but a confidence score does not explain why developers reject them and may be poorly calibrated across issue types. Option C produces more text without adding a stable dimension for aggregation. Option D removes useful category information and makes systematic analysis harder. A normalized detected_pattern field enables dashboards, pattern-level dismissal metrics, representative-example sampling, and controlled evaluations of prompt changes while retaining the broader category for higher-level reporting.

================