An AI review finding is a hypothesis, not a verdict. Engineering teams can evaluate it consistently by checking the evidence in the change, comparing the suggestion with code intent and project standards, testing whether the proposed action is clear and proportionate, and then triaging the risk. The workflow below turns those questions into a repeatable decision path without relying on claims about any particular review product.
1. Start with evidence and code intent
Read the changed lines together with the surrounding function, its tests, the pull-request description, and any relevant project guidance. Code-review practice should keep attention on defects, readability, and maintainability, while tests help establish the intended use cases [1]. Ask what the author meant to change, what contract the code is meant to satisfy, and whether the finding points to a concrete behavior, risk, or maintenance cost. If the finding cannot be connected to an observable issue or a documented project expectation, mark it for clarification rather than accepting it on the tool’s authority.
2. Test actionability and explanation quality
A useful finding explains precisely what is wrong, why it matters in this code, and what a reviewer or author could do next [1]. Check whether the suggested fix is small enough to review safely and whether it preserves the change’s intent. Prefer a suggestion that names the affected path, explains the consequence, and gives a proportionate next step over a vague instruction such as “simplify this.” Feedback should be phrased as a suggestion rather than a demand so the author can understand the path to approval [1].
3. Detect false positives with context
Before changing code, try to falsify the finding. Run the relevant tests and inspect the edge case the finding names; then check the commit message, pull-request context, nearby precedent, and the project’s documented standards. A suggestion may be a false positive when the behavior is intentional, the surrounding constraint makes the pattern necessary, or the same accepted pattern appears elsewhere. It may instead reveal a test gap when the current suite passes but does not exercise the claimed case. Record which evidence led to accept, reject, or defer so the decision can improve future review guidance.
4. Triage priority and risk
Order findings by the consequence of missing the issue and the consequence of applying the fix incorrectly. Investigate security defects and logic errors first because they can affect correctness or exposure; next examine performance findings with a benchmark or other relevant measurement; then address maintainability and readability findings according to team standards. A minor style preference should not displace a structural or logical problem, and a large risky refactor should not be the default response to a small issue. After any accepted change, rerun the relevant tests and review the resulting diff before merging.
A repeatable checklist for every AI finding
Use this decision flow: (1) What exact behavior or standard does the finding identify? (2) What code, test, or project evidence confirms or disproves it? (3) Does the explanation identify the consequence and a clear next action? (4) Is the suggested fix proportional and consistent with the project? (5) Is the risk of ignoring it higher than the risk of changing it? (6) After the decision, did the relevant tests pass and did a human review the final diff? Record the outcome as accept, reject, or defer with one sentence of evidence. Small changes and focused reviews make feedback easier to evaluate [1][3].
Sources and scope
This guide uses the supported code-review practices captured in the completed evaluation report. The report’s available source links are preserved below for the Microsoft/code-review practices claims. It intentionally omits the report sections marked unverified about a teardown, product capabilities, accuracy, speed, competitive results, and signup. The framework here is a team decision process, not a claim that any particular tool is correct or faster.
Source [1] → Michael Greiler — Code Review Best Practices, Michael Greiler — Code Review Practices at Microsoft