AI Code Review Best Practices
Blog post from Aviator
AI-generated code increases the burden on reviewers because machines can produce changes faster than humans can thoroughly assess them, while repeated AI iterations may introduce additional security risks. Effective review should begin with human-approved scope and concrete acceptance criteria, keep pull requests small, combine deterministic tools such as linters and security scanners with AI feedback and human judgment, and verify behavior through execution or preview deployments rather than relying solely on code inspection. Teams are encouraged to encode recurring review expectations as reusable rules, maintain human accountability for shipping decisions, and preserve audit records for compliance purposes. The article distinguishes AI code review, which infers intent from diffs and can be inconsistent, from verification, which tests implementations against predefined criteria, and presents Aviator Verify as a tool intended to automate parts of intent capture, behavioral testing, rule checking, evidence collection, and audit tracking without replacing existing tools or human reviewers.
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