Using AI Code Review Tools Without Losing the Human Pass
AI code review catches the easy stuff fast, but it also confidently approves bugs. Here is how I split the work between machines and humans. I have been using AI code review tools on my pull requests for about eighteen months. The promise is real: they catch obvious issues in seconds, they never get tired of checking naming conventions, and they give first-pass feedback before a human reviewer opens the diff. The disappointment is also real: they miss subtle bugs, they confidently approve things that break, and they occasionally suggest changes that make the code worse. Here is how I use them without losing the value of a human review. Treat the AI Pass as a Linter, Not a Reviewer Early on I treated AI code review the way I treat a senior reviewer: I read the comments, I trusted the intent, I made the change. That went badly on at least three occasions. The AI confidently suggested extracting a method that would have hidden a side effect, inlined a loop that was deliberately kept verbose for readability, and approved a change that introduced a null pointer because the variable name looked fine. Now I treat the AI pass the way I treat a linter. It catches formatting issues, naming inconsistencies, missing edge cases in obvious ways, and low-hanging concerns like unhandled promise rejections. I act on those instantly.