AI-generated code is a draft from a confident but fallible author. It often looks correct and compiles, yet hides subtle bugs, invented APIs, or security holes. A disciplined review keeps the speed without the risk.
AI-generated code is a draft from a confident but fallible author. It often looks correct and compiles, yet hides subtle bugs, invented APIs, or security holes. A disciplined review keeps the speed without the risk.
1. Read the diff fully → do I understand every line?
2. Does it solve the real problem, including edge cases?
3. Run: tests + linter + type checker
4. Scan for security + performance red flags
5. Only then: commit (with a message that reflects what it really does)
Treat AI like a fast junior developer: helpful, productive, but whose output you always review before it lands. The responsibility for shipped code stays with you, not the model.
The danger of AI code isn't that it's obviously broken — it's that it's plausibly broken, passing a casual glance while harboring a bug or vulnerability. Reviewing every line, running tests, and refusing to ship code you don't understand is what separates using AI as a force-multiplier from quietly accumulating technical debt and security risk you can't account for.
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