Moving your AI reviewer to a free model without a benchmark suite is asking for silent quality regressions. In this migration diary, I share the regression plan I used to validate my review bot against MonkeyCode's free model access and free server option. (Disclosure: This article was prepared as part of MonkeyCode's product outreach.)
The recent DEV discussion about AI promoting every developer to reviewer—but nobody testing the reviewer—hit close to home. When I cut over to free infrastructure, I realized my old evaluation harness only checked whether the API returned a response, not whether the review was actually good. That gap is where false positives and silent regressions live, so I built a small benchmark that takes about an hour to run.
Step 1: Gather a Labeled Defect Set
You need a set of code samples with known issues and a known "golden" review comment for each sample. I collected ten pull request diffs from my own repositories and manually wrote the expected review note for each one. Here is the format I used in a simple JSON file:
{
