Knowing how to review AI-generated code starts with a practical question: does this change meet the requirement without breaking anything the application still needs? A polished explanation and a passing test run are useful evidence, but neither answers that question alone.
First, establish the intended behavior. Then inspect the actual changes, challenge the tests and record a decision. This checklist gives a development team a repeatable review process, with a small authorization example to show what a useful check can uncover.
How to review AI-generated code for scope
Read the task before reading the proposed solution. Write down the outcome, the affected feature and the behavior to preserve. Otherwise, the implementation can quietly become the definition of success.
Next, compare the changed files with that scope. A navigation fix might reasonably touch a component and its tests. A new deployment script or an unrelated dependency needs its own explanation. Ask why each additional file changed before accepting the whole patch.
For a large change, request smaller reviewable parts. Each part should have a coherent purpose and leave the project in an understandable state. This also makes later corrections easier to locate.
Check the behavior the change must preserve
Run the original reproduction steps against the proposed fix. Also check a nearby workflow that should remain unchanged. For example, a repaired mobile menu still needs working desktop links and keyboard focus.
Keep the baseline visible. If a check already failed before the change, record that fact separately. However, an existing failure does not excuse a new regression. The reviewer needs to distinguish the two.
Check what happens when an operation fails halfway through. Does the interface show an accurate state? Can the user retry safely? These questions often reveal more than repeating the successful path.
How to review AI-generated code at a trust boundary
Trace important input from its source to its effect. In a web application, that may mean following a request parameter through validation, permission checks, a database query and the response. Check each step in the context of the application.
The OWASP secure code review guide explains why manual review matters for business rules and trust boundaries. Automated scans can help identify suspicious patterns; a person still needs to assess what access the product actually intends.
For instance, hiding an edit button does not establish a server-side permission boundary. Test the request directly in an authorized test environment. Use synthetic accounts and records so the review cannot expose another customer’s data.
Use tests that can reject the proposed fix
Read the assertions, not just the test names. A test called “denies unrelated users” is useful only if it actually checks a denial. Also inspect changed fixtures and mocks: they may have removed the condition that caused the original failure.
Add a case derived from the requirement before studying the implementation in detail. As a result, the expected outcome has an independent basis. If the new test accepts both the old defect and the proposed fix, it does not prove the repair.
- Normal use: the intended user completes the action.
- Boundary: an empty, missing or out-of-range value produces the specified result.
- Denied action: an unrelated account cannot cross the access boundary.
- Failure and retry: a repeated request cannot create an unintended second effect.
These cases are starting points. Choose the ones that fit the change, and add checks for concurrency or recovery when those behaviors matter.
Check dependencies and operational effects
A small library can introduce a large maintenance obligation. Therefore, verify the package name, source, version and reason for adding it. Inspect the lockfile too, then run the project’s normal dependency and security checks.
Also look beyond the main function. A change can alter configuration defaults, logging, data retention or deployment requirements. For example, logging a complete request may expose information that the feature never needed to retain.
If the patch changes stored data, establish how it behaves during rollout and rollback. Keep this review specific to the proposed change. A routine copy edit does not need a database migration exercise.
Reviewing AI-generated code: a worked example
Consider a hypothetical project tool. Its stated policy allows an editor to change a project only when that editor belongs to the project. The following simplified proposal checks only the global role:
allow_edit = user.role == "editor"
That expression can accept an editor from an unrelated project. The missing condition is project membership. Before choosing an implementation, write the expected decisions:
- Editor assigned to this project: allow.
- Editor assigned only to another project: deny.
- Viewer assigned to this project: deny.
- No authenticated user: deny.
Then inspect the real request handler, not only its helper function. It must use the authenticated identity and membership from trusted application state. A client-supplied membership flag would undermine the intended check.
Finally, run these cases against the full request path and verify that denied requests leave the record unchanged. This is an illustrative policy, not a complete authorization design: the real product may also have administrator, ownership or organization rules.
Record a decision that others can follow
A useful review states what you checked, what passed and what remains uncertain. Link findings to the affected lines or behavior. When requesting a correction, describe the failed requirement and a way to reproduce it.
GitHub’s pull request review documentation distinguishes comments, approvals and requests for changes. Use the decision that reflects the evidence. A comment saying “looks good” should not conceal a required correction.
AI review tools can provide another perspective. However, validate their findings before accepting a suggestion. GitHub’s application card for Copilot agents describes human oversight and the limits of generated review feedback.
Deciding how to review AI-generated code is ultimately a delivery decision: connect the requirement, the diff and the observed result. For practical implementation support, explore Yasa’s development and integration services.
Get a second set of eyes on your change
Have a patch that is difficult to assess? Tell Yasa what it should do, where it runs and which behavior must stay intact. We can discuss a focused review and the next useful step.
