Define useful before tuning volume
A quiet review can miss a serious bug. A long review can contain nothing actionable. Comment count alone therefore tells you very little about quality. Define the kind of finding you want before deciding whether to raise a severity threshold or hide a category.
A useful finding identifies a changed behavior, a plausible and specific trigger, and an impact that matters for the intended system. It gives the author enough evidence to reproduce the concern or explain why it cannot occur. That definition makes quality something a reviewer can assess rather than something inferred from how confidently a model writes.
Distinguish an observation from a defect
Consider the statement that a function reads an entire file into memory. That may be an accurate observation. To make it a performance finding, a reviewer needs a relevant bound, workload, or failure mechanism. Without that connection, the comment asks the author to redesign code in response to an unspecified possibility.
Similarly, removing a component does not prove that a supported feature has been broken. The component may be obsolete or intentionally replaced. A finding should point to the caller or requirement that still needs it. When that evidence is absent from the diff, the right next step may be a question for a human reviewer.
Use a reference check and a reasoning check
The reference check asks whether the file, line, and quoted code are real. The reasoning check asks whether that code causes the stated problem. Keep the two checks separate. An exact quote can support a mistaken conclusion, while a correct concern with an invented location is difficult to act on reliably.
AntiCode validates AI finding references against the supplied diff and rejects the response when the evidence cannot be verified. The quote is used for validation rather than stored as an extra finding field. This helps prevent fabricated locations from becoming saved results, but human review is still needed to assess the proposed failure.
Challenge the finding with a counterexample
Before changing code, look for the condition that would make the warning false. Is the input already constrained? Does an authorization check reject the operation deliberately? Is a retry part of the expected flow? Is the expensive operation bounded by a limit that the finding ignored? This is an efficient way to distinguish a defect from an incomplete reading.
Google's guidance on review comments recommends explaining the reasoning and making the intent of a comment clear. Apply that discipline when forwarding AI findings to an author. State whether you have reproduced the issue, need clarification, or are suggesting an optional improvement. Do not present an unverified model claim as an established incident.
Tune preferences after understanding the pattern
Use personal triage to record which findings you accept or dismiss. If a recurring class is irrelevant, investigate why before hiding it. A generated directory may justify an ignored path. Repeated incorrect assumptions about a validation layer suggest a review-quality problem that a severity filter will merely conceal.
AntiCode's display preferences help organize the findings you see; they do not rewrite the canonical review for other authorized users. Keep a small set of representative useful and unhelpful examples outside the dashboard for evaluating your process. Compare future reviews against that set, checking both false alarms and known defects that the reviewer misses.
After a real fix, review the new revision and test the original trigger. The goal is a clearer connection between evidence and decisions. A reduction in warning volume is valuable only when the team still receives the information it needs to make those decisions well.
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.