How I Review AI-Generated Pull Requests (A Step-by-Step Checklist)

AI-generated code is showing up in pull requests everywhere. Whether it's from Copilot, Claude, or a teammate who used ChatGPT — you need a review strategy that catches the specific failure modes AI code tends to have. Here's the checklist I use. It's different from how I review human-written code. The Problem With Default Reviews Human code has human failure patterns: inconsistent naming, forgotten edge cases, copy-paste errors. You know what to look for because you've made the same mistakes. AI code has different failure patterns: Plausible but wrong logic — it looks correct on first read but handles an edge case backwards Hallucinated APIs — function calls to methods that don't exist in your version of the library Over-engineering — adds abstraction layers nobody asked for Silent behavior changes — refactors adjacent code that wasn't part of the task Missing error handling — the happy path works perfectly; everything else crashes Your review process needs to target these specificall