> Here’s a third one. In some of the code reviews I’ve encountered that AI gives a lot of feedback, it’s just providing noise
Something I've found fun is seeing how long it takes for an llm review tool to come back satisfied with a PR. Think 100 lines of code changed, nothing terribly significant, but also not trivial. I'll have a local Claude session setup to babysit the PR and wait for feedback, accept all the recommendations, push the change up and request a review. I cap the number of iterations at 10 just so I'm not blowing a stupid amount of money. I've yet to come up with a PR where the llm reviewer is satisfied with the changes and has _no feedback_.
So where's the reasonable cutoff point for llm based reviews?
Huh, I've had many times when `codex /review` comes back satisfied on the first shot, both with handwritten and LLM-assisted PRs.
We use only one round of LLM review, and have discussed as a team still assessing recommendations, not just accepting everything blindly. So somewhere in the (0,1] rounds of review. Sounds like our preferred ratio of human to LLM involvement is different than yours, though.
Something I've found fun is seeing how long it takes for an llm review tool to come back satisfied with a PR. Think 100 lines of code changed, nothing terribly significant, but also not trivial. I'll have a local Claude session setup to babysit the PR and wait for feedback, accept all the recommendations, push the change up and request a review. I cap the number of iterations at 10 just so I'm not blowing a stupid amount of money. I've yet to come up with a PR where the llm reviewer is satisfied with the changes and has _no feedback_.
So where's the reasonable cutoff point for llm based reviews?
We use only one round of LLM review, and have discussed as a team still assessing recommendations, not just accepting everything blindly. So somewhere in the (0,1] rounds of review. Sounds like our preferred ratio of human to LLM involvement is different than yours, though.