How to Review AI-Generated Code Like a Human Reviewer
I tested this myself. Here is the honest take.
What are the most common AI code mistakes?
The most common mistake is inconsistent patterns. I found 5 different error handling patterns in one project — try/catch in one file, .catch() in another, nothing in a third. Fix: add pattern enforcement to .cursorrules before generation, check consistency during review.
How long should an AI code review take?
My review time: 5-10 minutes for standard features, 15-20 for security-sensitive. A checklist reduced my time by 40% and improved catch rate. Without it, I missed issues. With it, I catch them consistently.
What is the best review workflow?
Review workflow: 1) Run automated linter. 2) Check error handling consistency. 3) Verify input validation. 4) Check for hardcoded secrets. 5) Confirm tests pass. 6) Review architecture fit. This catches about 80% of AI-specific issues.
AI code review checklist: consistent error handling? Hardcoded secrets? Input validation? Rate limiting? Naming conventions? Unnecessary dependencies? Tests pass? This catches 80% of AI issues.
Reviewing AI code differs from human code. Human code has intentional structure. AI code has accidental correctness — it works but the architecture is fragile. Review for maintainability, not just correctness. If you cannot understand it, reject it.
I need to [task]. Give me the exact steps and common mistakes.
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