The short answer
Review AI-generated code against the same product requirements and production risks as any other change. Confirm that it solves the intended problem, respects permissions, handles failures and can be maintained by the team. A passing build is a useful checkpoint. It does not establish that the behaviour is correct or the release is ready.
Why review deserves more attention
AI assistance can make a large amount of code available for review very quickly. That shifts attention towards how changes are explained and verified. GitHub’s review guidance emphasises functional checks, project context, human oversight and testing. Read GitHub’s review guide.
The practical question for a product owner is simple: can the engineer responsible for this change explain what it does, how it was checked and how it could fail? If the answer depends entirely on the tool’s own explanation, more review is needed.
Write the acceptance criteria before the patch
Take an illustrative account-invitation feature. The requirement is more precise than “send an invitation”: only an authorised administrator can invite a person, the invitation belongs to the correct organisation, expiry works, and a repeated request does not create conflicting accounts.
Write those behaviours down before reviewing the implementation. Then ask the reviewer to demonstrate each one. Keeping the criteria separate from generated code helps avoid a circular check in which generated tests merely confirm the assumptions made by the same tool.
Inspect the boundaries that carry risk
- Identity: where is the user authenticated and where is their permission checked?
- Data: can a request read or change another customer’s records?
- Failure: what happens after a timeout, partial write or duplicate request?
- Dependencies: is each new package necessary, maintained and actually the intended package?
- Operations: what information will support staff have if the feature breaks?
These questions are a suggested review structure. The depth should follow the consequences of the change: payment handling and account permissions need more scrutiny than a reversible visual adjustment.
Check the development environment too
OWASP’s Secure Coding with AI guidance addresses risks introduced when coding tools can run commands, install dependencies and access connected systems. Limit unnecessary access and treat instructions from untrusted project content cautiously. Read OWASP’s guidance for AI coding tools.
Our proposed release routine is to inspect the final diff, run the checks that exercise changed behaviour, review any new dependencies and rehearse the rollback when the change affects stored data. Keep the change small enough that a human can meaningfully understand it.
Keep a named owner for the result
The review record should state what was changed, what was tested and what uncertainty remains. The engineer approving the release owns that judgment. AI assistance is part of how the team works; responsibility for the delivered software stays with people.
Can another AI review the change?
It can provide an additional perspective, but it does not replace an accountable reviewer or evidence from the running system.
Should every change need a large test suite?
No. Choose checks that address the actual risks. Avoid adding tests that restate implementation details without proving useful behaviour.
See how CloudCoder builds and verifies software, or read our introduction to AI-assisted development.
