Why AI code needs review
AI can generate working-looking code quickly, but it can also produce code that is subtly wrong, insecure, inefficient, or inappropriate for your project. Merging it without review is risky, because the AI does not truly understand your system's requirements or constraints. Treating AI-generated code the same way you would treat code from any contributor, with a thorough review before merging, is essential to keep quality and security intact.
Check correctness first
The first thing to verify is that the code actually does what it is supposed to, correctly, including edge cases. AI code can look plausible while containing logic errors or handling only the obvious case. Read it carefully, trace what it does, and confirm it matches the intended behavior. Do not assume it is correct just because it runs; running without error is not the same as being right.
Review security and dependencies
Check for security issues: does the code validate input, handle secrets properly, and avoid introducing vulnerabilities? AI may produce insecure patterns if not guided carefully. Also scrutinize any new dependencies it adds, confirm they are necessary, trustworthy, and compatible, rather than pulling in unneeded or risky packages. Security and dependency review is where AI-generated code often needs the most scrutiny, since mistakes here can be costly.
Verify tests and performance
Confirm that the code is covered by meaningful tests, and be wary of AI-generated tests that merely restate the implementation or pass without checking anything real. Run the tests and consider edge cases they miss. Also consider performance: does the code do anything needlessly slow or wasteful? AI may choose an inefficient approach. Verifying both real test coverage and reasonable performance protects against problems that correctness review alone can miss.
Mind licensing and keep ownership
Be aware of licensing: AI-generated code could resemble licensed code, so for anything important, ensure you are comfortable with its origin and your right to use it. Above all, keep human ownership: a person should understand, take responsibility for, and sign off on merged code, through a normal pull-request review. The AI assists, but the human remains accountable for what goes into the codebase.
The bottom line
Review AI-generated code before merging with a checklist: verify correctness and edge cases, scrutinize security and new dependencies, confirm meaningful tests and reasonable performance, and mind licensing. Run it through a normal pull-request review where a human understands and owns the result. Understanding this keeps AI's speed from undermining your code's quality, security, and accountability.
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See also: AI coding assistant vs traditional IDE autocomplete, AI-generated app security: what to check before launch.
Sources
Published date reflects the original event date (2026-10-23). This article is original Skillo editorial written from the sources above; facts were verified in September 2026.
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