Ai Coding And Product Systems medium
Your AI code review bot posts about 15 comments per pull request. An audit finds roughly 40% of comments are wrong, and developers now bulk-resolve bot comments without reading them. What change most improves the system's real value?
- A Increase the number of comments per PR so that genuine issues are statistically more likely to appear among the flagged items.
- B Switch to a larger model for every comment while keeping the same volume, since better reasoning will fix the incorrect findings.
- C Add a disclaimer to each comment noting it is AI-generated, so developers calibrate their trust and read every finding carefully.
- D Score each finding's confidence, post only those above a tuned threshold, and track the dismissal rate as a precision metric.
Answer: D. Score each finding's confidence, post only those above a tuned threshold, and track the dismissal rate as a precision metric.
Review tools live or die on **precision**. Once more than about 1 in 3 comments is wrong, developers ignore all of them, so it is worth trading some recall for fewer, trusted comments. A larger model (B) is tempting, but without a confidence threshold the volume of residual noise still trains developers to dismiss everything. Accepting lower precision makes sense only for narrow high-stakes checks, such as security-sensitive paths, where a missed bug costs far more than a false alarm.
One system design question a day, with spaced repetition so the ones you miss come back.
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