AI-assisted guide · Reviewed before publication · October 22, 2026
A code review comment can identify a concern without spelling out how to check it. AI can help brainstorm possible checks from a short comment and relevant code excerpt. The useful result is a draft to review—not a verdict that the code is correct or the tests have passed. Keep the context narrow, check every suggestion against your project, and run any tests yourself.
Start with a small, safe context
Choose one review comment and the smallest code excerpt that helps explain it. Add only relevant details, such as the intended behavior or an existing test name. Remove credentials, personal information, secrets, and unrelated repository content. Follow your organization’s rules for using AI tools; if you are unsure whether content may be shared, leave it out or ask the appropriate person. A focused prompt gives the model less room to guess. If the comment depends on surrounding code or a project convention you have not provided, ask the model to list what context is missing rather than filling in the gaps itself.
Ask for checks, not a judgment
Request a compact list that connects each concern to a possible check and a point for you to verify. Useful categories might include expected behavior, an edge case, or an existing test to inspect—but let the code and comment determine what belongs. Ask the model to distinguish suggestions from facts and to flag uncertainty. A consistent structure makes a draft easier to scan. For instance, ask for three fields: “review concern,” “possible check,” and “what I should confirm manually.” Structured output can help keep information in a requested format; a tidy format does not establish that any suggestion is technically correct.
Review the draft against the change
For each proposed check, compare it with the actual code, the review comment, your project’s conventions, and the tests that already exist. Remove items that are unrelated, duplicate existing coverage, assume behavior the code does not promise, or introduce requirements that nobody asked for. If a suggestion depends on missing context, investigate that context yourself instead of treating the model’s assumption as fact. An illustrative example: a reviewer says, “What happens if this optional field is absent?” A possible checklist item might be to inspect the code path when the field is missing and consider a test for that case. Whether that is the right test depends on the function’s intended behavior and the surrounding implementation. The example is not a claim about a particular codebase.
Run checks yourself and keep the loop useful
Treat the accepted checklist as a reminder of what to examine, not evidence that anything has been validated. Run relevant checks in your normal development environment and interpret the results yourself. Do not say that a test passed unless you actually ran it and saw the result. Models can produce different responses, and their suggestions may be incomplete or wrong. For repeated use, try the prompt on a few representative review comments and adjust it when it regularly misses the kind of detail you need. Keep examples anonymized and use only material permitted by your team’s rules.
