AI code review habits for generated code
AI-generated code can pass tests and read cleanly while hiding context mistakes, weak coverage, or risky abstractions. Strong AI-generated code review starts by reading the task brief, then checking diff shape and the test evidence that proves the change works. These review habits and review techniques turn AI code critique into a repeatable check on ownership, not a rubber stamp.
Review the work, not the demo
AI-generated code can look polished while hiding context mistakes, weak tests, or risky abstractions. Reviewers need to inspect the task brief, diff shape, verification evidence, and ownership boundary before they judge whether the change is acceptable.
The review loop we teach
Teams practice small diffs, targeted tests, failure reproduction, second-pass critique, and Codex code review prompts that force the agent to explain risks instead of simply defending its own output.
What becomes repeatable
The team leaves with a review checklist, examples of acceptable evidence, escalation rules for architecture and security decisions, and a shared vocabulary for rejecting unreviewable agent work early.
Official references
Current product documentation we use when shaping this training topic.
Related training topics
Bring this into your team
We tailor the training to your codebase, adoption stage, and review standards.
Get in touch