This guide to ai-assisted code review: a checklist for better results explains the core concepts, practical decisions, and common mistakes to watch for. Use the steps below as a starting point, then verify details against the tools and requirements in your own workflow.
Ask for specific classes of risk
Give the assistant the diff, relevant interfaces, and expected behavior. Ask it to identify correctness bugs, security concerns, unhandled errors, performance risks, and missing tests. Request file and line references so each finding can be checked.
Separate findings from speculation
A useful review explains the triggering condition and likely impact. Reject vague warnings that do not connect to the code. Verify each finding by tracing the control flow, reproducing the issue, or writing a focused test.
Use deterministic checks too
Static analysis, dependency scanning, type checking, and automated tests catch different classes of problems. AI review complements these tools but does not replace them. Protect private code and follow your organization’s rules before sending diffs to a service.
Practical checklist
Use AI to generate review hypotheses, then validate them with code inspection and tests.
Editorial note: This educational guide explains general methods and does not claim independent product testing. Check official documentation for current features, pricing, and terms before making decisions.
