Start with a claim you can test
An AI review is useful when it gives a developer a specific claim to investigate. A confident paragraph is not enough. The reviewer needs to identify the changed behavior, the input or state that activates it, and the consequence that makes it worth fixing. Treat that explanation as the beginning of a review conversation.
Imagine a change replaces an explicit numeric parser with dynamic evaluation. A useful finding explains how externally supplied text reaches that operation and what execution becomes possible. A weak finding merely says that the function looks risky. Both can point at the same line; only one gives the author a clear route to verification.
Check evidence before discussing severity
Open the cited file at the reviewed revision. Confirm that the line exists and that the quote matches the code. Then read enough surrounding code to trace the input and any guards. A nearby validation function may make the suggested trigger impossible. An exception may deliberately reject an invalid request.
AntiCode requires AI findings to reference a source line in the supplied diff. It checks the path, line number, and quoted evidence before saving results. This catches a class of reference errors. It cannot establish that the reasoning is correct, that unseen callers behave as assumed, or that the suggested fix preserves the product's requirements.
Ask for a trigger, a mechanism, and an impact
A review discussion becomes easier when the team uses three questions consistently. What starts the failure? Which changed operation causes it? What observable behavior follows? If one answer depends on missing context, ask the author or inspect that context before treating the issue as confirmed.
Consider a warning about rejecting an expired authorization token. The rejection may be the intended security boundary. The real review question is whether a valid user can recover through the supported renewal flow. Removing the check would make the error disappear while potentially making the system less safe.
- Trigger: identify a concrete input, account state, or sequence of operations.
- Mechanism: connect that trigger to the changed code rather than an imagined implementation.
- Impact: explain the incorrect result, exposed data, or broken user action.
Keep the approval decision with a person
Google's code review guidance emphasizes engineering judgment and improving code health without demanding arbitrary perfection. Apply that standard to machine-generated advice too. A theoretical redesign is not automatically a reason to block a focused fix. Neither is a stylistic preference disguised as maintainability.
Use tests to challenge the claim when a focused test is feasible. A test that reproduces the old failure and passes with the proposed fix is stronger evidence than a rewritten comment. Review the test itself: it should exercise the real boundary, not a simplified substitute that makes the finding appear correct.
Finish the loop on the current revision
In AntiCode, a managed AI review is started manually for a selected pull request. Read the results, make your decision, and use personal triage to organize the findings. An accepted finding is a recorded judgment; it does not apply a patch, approve the pull request, or prove the issue has been fixed.
After the author changes the code, sync and review the new revision. Keep your final approval tied to the code that will actually merge. AI can help direct attention, but the durable output of a review remains a defensible human decision and a change whose behavior the team understands.
Further reading
Put it into practice
Start with a small change you are authorized to review. Follow the AntiCode setup guide, inspect each finding, and keep your tests and human approval in the loop.