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How to Build a Zero-Cost AI Code Review Audit Loop With Measurable Accuracy

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A reproducible patch audit loop can help developers measure how accurately AI tools review generated code, addressing a largely untracked gap in modern workflows. The system runs on a free server using free token allowances, making it accessible to solo founders and small teams. It tracks five key metrics — catch rate, precision, patch coverage, latency, and token cost — to give a clearer picture of reviewer reliability. The audit works by extracting diffs between commits, passing them to an AI review tool, and comparing outputs against a ground-truth case file built from a project's own git history. The goal is to treat the AI reviewer as a testable component rather than an assumed reliable one, reducing the risk of shipping bugs that slipped through unchecked reviews.

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How to Build a Zero-Cost AI Code Review Audit Loop With Measurable Accuracy · ShortSingh