AI code review habits for generated code
Practical code review habits for AI-generated code, including risk checks, diff review, tests, ownership, and team-wide standards.
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 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.
Related training topics
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We tailor the training to your codebase, adoption stage, and review standards.
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