Most defects missed in code review are logical errors and edge cases — not formatting issues, not naming conventions. Google's "Modern Code Review: A Case Study at Google" (Sadowski et al., 2018) examined review practices at scale, and since then the volume of AI-generated code has grown while reviewers still spend the same 15–30 minutes per PR. Below is how to structure AI code review across four categories: correctness, security, performance, readability. Priority is in exactly that order. For each category: a checklist, an LLM prompt, and real finding examples. At the end — CI pipeline integration. Why Category Order Matters A typical code review starts at the surface. The reviewer notices a poorly named variable, suggests a refactor, discusses style. That consumes 80% of the time. Logical errors and security issues go unnoticed. A fixed order solves this problem: Correctness — does the code do what it claims? Are edge cases handled? Security — any injections, data leaks, or authorization issues?…