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A customer wants AI code review on every PR. What does it catch, what does it miss, and how do you stop the team turning it off?

Nobody turns off an AI reviewer because it missed a bug. They turn it off because it left nine comments they had to read and three that mattered. Precision is the design variable, and the arithmetic says cutting comments per PR from twelve to four costs you 7% of the real findings and two thirds of the triage time.

Updated Sep 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.

Nobody turns off an AI reviewer because it missed a bug. They turn it off because it left nine comments they had to read and three that mattered. Precision is the design variable, and the arithmetic says cutting comments per PR from twelve to four costs you 7% of the real findings and two thirds of the triage time.

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FEDITOR'S NOTE

The tell for a candidate who has actually rolled this out is that they optimize precision and treat recall as secondary, which is the opposite of the instinct. A reviewer that finds every bug and buries them in noise gets muted in three weeks, after which its recall is zero. The second tell is explaining the catch-versus-miss boundary with a mechanism rather than a list: the model sees the diff and the files it can pull, so anything whose evidence lives outside that window is invisible no matter how good the model is.

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