AI code review habits for generated code

The best way to implement code review habits for AI-generated code is to review the task brief, diff shape, test evidence, and ownership boundary before merge. AI-generated code can pass tests and read cleanly while hiding context mistakes, weak coverage, or risky abstractions, so reviewers need a repeatable AI code critique workflow rather than a rubber stamp.

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How should a team review AI-generated code before it ships?

Cursor Workshop uses four stops before merge: the brief is attached, a test ran and the link is in the pull request, a person who did not prompt the agent read the diff, and architecture or data changes stay with a human owner. If any stop fails, the change does not ship.

Review the real work

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 Cursor 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.

Have you tried these?

Run the change on one branch. The methodology puts that in Review: an engineer still owns the merge. Training is the team version of that step.

Bring this into your team

We tailor the training to your codebase, adoption stage, and review standards.