AI-generated code can look convincing even when it contains subtle bugs. Before using it in a real project, which parts do you inspect most closely, and what kinds of mistakes are easiest to miss?
4 Answers
Honestly, review all of it. AI can produce code that looks polished while making incorrect assumptions, so I would not treat any line as trusted without understanding and testing it.
Start with the task definition and the generated result. Clear requirements, tests, and hands-on feature checks are all important because a solution can pass a narrow test while still failing in real use.
Be extremely cautious with financial calculations and other high-stakes logic. I would independently verify the formulas and assumptions rather than relying on generated code—or even generated tests—to prove they are correct.
Pay especially close attention to security-sensitive and user-facing areas: input validation, access control, error handling, accessibility, responsiveness, and performance. Those issues can be easy to miss when the code works in a basic demo.
That makes sense—checking whether it actually solves the intended problem seems just as important as checking whether it runs.