AI Code Review Best Practices: Getting Value Without the Noise
AI code review tools comment on everything, which buries the useful feedback. Here is how I configure them to surface what matters. AI code review tools promise to catch issues before a human reviewer opens the pull request. In practice, the default configuration comments on style nits, obvious cases, and a lot of things I would have caught with a linter anyway. The signal gets buried in noise. After running these tools across dozens of pull requests, I have a set of practices that make them genuinely useful. The core insight is that the default configuration is designed to demonstrate capability, not to fit into a real workflow. You have to tune it. Disable What Your Linter Already Catches The first thing I do is turn off the categories my static analysis tools already cover. If eslint or golangci-lint already enforces naming and formatting, the AI reviewer does not need to repeat those comments. Overlap is not helpful, it is noise. I configure the AI tool to focus on categories that require judgment: inconsistent error handling, missing edge cases, tests that do not exercise the code under test, and logic that looks correct but has a subtle bug. This single change cut the comment volume on my pull requests by more than half, and the remaining comments were the ones I actually wanted to read.