How to check AI-generated code

Treat the change as a proposal

AI-generated code is a proposed change to evaluate against the task and the project, not proof that the task is done. Start by identifying what the change is meant to accomplish, which files it affects, and what existing behavior or conventions it should preserve. A code review compares the change with its purpose and surrounding design; security review also considers data flow, application logic, and context.

Use the repository’s documentation and nearby examples to understand the intended design. Then read the changed code yourself, including names, error handling, and how the change fits with the rest of the program. Automated tools can help surface issues, while human review is useful for intent, business logic, and context-specific risks.

Review in a useful order

First, compare the change with the requested behavior and project conventions. Check which files and components changed, whether the implementation solves the stated problem, and whether it follows established patterns. This gives you context for the rest of the review and helps you focus attention on high-risk changes.

Next, inspect the code for logic errors, missed requirements, and edge cases. Check that inputs are validated, errors are handled, and security controls such as authentication and authorization still apply where relevant. Look closely at new dependencies: verify that each package exists, is maintained, and has a license compatible with the project.

Then use the project’s own checks to see whether the change builds and behaves as expected. Run relevant existing tests and static analysis, and add or adapt tests for important expected behaviors and boundary cases. For a security-sensitive change, review how data moves through it and consider a focused manual security review alongside automated checks.

Common ways a review can miss problems

Code can look plausible while solving the wrong problem, ignoring a constraint, or using an API that does not exist. Check the actual behavior and the project’s requirements rather than relying on how polished the code appears. Also inspect the full change: a test that was deleted or skipped may hide a failure instead of fixing it.

A green test run does not show that every relevant case was tested. Tests generated with AI may miss scenarios, and a successful run only describes the tests that were actually run. Automated security checks have limits too: manual review can find issues in application logic, complex security work, and context-specific behavior that automated tools may miss.

A review can also overlook a newly introduced package, an incompatible license, or changes to security controls outside the obvious lines of code. For larger or sensitive changes, get another reviewer when possible and make unresolved concerns visible instead of treating tool output as approval.

Check whether the result is ready

After review, you should be able to explain how the change meets the task and how you checked its behavior. Confirm that the relevant tests and analysis ran, look at their actual results, and verify that important edge cases are covered. If tests were added, inspect their assertions and make sure they test the intended behavior rather than merely repeating the implementation.

Before accepting the change, revisit the diff for unrelated edits, skipped tests, new packages, and altered security checks. For sensitive code, trace inputs through the affected logic and check validation, authorization, and error handling. Record any remaining uncertainty and ask for a focused review where needed. The result is better supported when the code matches the requirement, relevant checks pass, and a person can explain why the checks address the risk.