AI-Generated Code Review: What to Check in a Pull Request

Reviewed by Product Specialist at thinQit. Updated 3 November 2026.

AI-generated code review is the practice of judging an AI-written pull request against the request that produced it, not against how plausible the code looks. thinQit, the AI delivery workspace built by thinQit B.V., pairs Cody, its AI developer, with a reviewer checklist covering scope, tests, boundaries and recorded decisions.

What is different about reviewing AI-generated code?

AI-generated code arrives fast, fluent and uniformly formatted, which removes the usual signals a reviewer relies on. A hesitant, messy diff from a junior colleague tells you where to look. A confident, tidy diff from an AI developer does not. The reviewer therefore needs a method that does not depend on the code looking suspicious.

The method starts from the request. A human colleague carries unspoken context, such as the rounding convention agreed last quarter. An AI developer only has what is in the repository and the request. That makes the request the specification, and the review a comparison between specification and result. Where the request was vague, the review will be vague too, which is why an unclear goal is a poor fit for any AI developer. Our walkthrough of how Cody turns a described change into a pull request explains the stages that produce the artefact you review.

It also does not change who is accountable. The person who merges owns the change. The AI developer proposes, and your own merge rules decide.

How do you review an AI pull request step by step?

Review an AI pull request in five passes: request, plan, diff, tests, and decision. Each pass has one question, and none needs the reviewer to have watched the session live. Cody works on a branch, shows every step as it happens and opens a pull request, so the history is already written down when the review starts.

1. 1Request. Re-read what was asked. Is the outcome stated so that someone else could tell whether it was achieved?
2. 2Plan. Compare the plan to the request. Does it name the files and behaviors you expected, and nothing more?
3. 3Diff. Does the diff touch only what the plan named? Unrelated formatting churn and surprise refactors are scope drift.
4. 4Tests. Do the tests describe the requested behavior, and did existing tests keep passing without being deleted or loosened?
5. 5Decision. Accept, request a revision, or defer with a named reason, and record it.

The order matters. Judging the diff before re-reading the request invites you to approve what the code does rather than what was needed.

Why are passing tests only the start of the review?

Passing tests prove what the tests cover and nothing else. On the Cody product page, runs are test driven: Cody writes the tests first and shows the results as evidence. That gives a reviewer an objective definition of done, because a test that fails before the edit and passes after it shows the behavior changed as intended.

The reviewer's attention then shifts from "did something break?" to "do these tests describe what we wanted?". That second question is quick for a human to answer and slow for a machine to answer on its own. Spend your minutes on it.

Three situations deserve a second look even when everything is green:

• The changed code path has no new test covering it.
• An existing assertion was weakened, skipped or removed to make the run pass.
• The tests mirror the implementation line by line, so they would pass even if the logic were wrong.

If a repository has thin coverage, the honest first task for an AI developer is to add tests around the area, review those, and only then change behavior.

How much autonomy should an AI developer get?

Give an AI developer as much autonomy as the change can safely absorb, and no more. Cody offers three decision modes: Plan, Ask first and Auto. In Plan you see the intended steps first. In Ask first it checks with you before it acts. In Auto it proceeds on its own within risk rules while still showing each step.

The mapping is a starting point, not a rule. The principle is graded autonomy, which we describe further in approval gates that make AI delivery safer. A small written list of what the AI developer may do alone, and what always needs a plan review, turns that principle into governance.

What should you look for that is not in the diff?

Some of the most important review findings are absences. A diff shows what was added or changed, so it takes deliberate effort to notice what should have been there and was not.

Missing handling for states

Ask which states the change must handle: empty, loading, error and success for an interface, or invalid input and permission failures for an endpoint. If the request did not name them, the generated code may cover only the happy path.

Boundaries that were crossed quietly

Check whether the change reaches into permissions, billing logic, credentials or other sensitive paths. Our guide to security guardrails for AI agents shipping production code goes deeper on this. For how thinQit itself protects stored credentials, access and audit logging, see the thinQit security overview.

Merge conflict decisions

When a branch has fallen behind, Cody pulls, merges and explains what it kept. A conflict resolved silently is a review risk; a conflict resolved with a written account of which side won and why is something you can verify. Read that explanation as carefully as the code.

How do you keep review findings from repeating?

Turn each correction into a rule that the next task can use. If a reviewer finds that a generated change loosened a test, the useful output is not only the fix but the rule: tests are never weakened without a named reason. Written down once, that rule applies to every later change instead of being rediscovered in another pull request comment.

This is the role of shared project context. In thinQit, Codex and its project context keep decisions, constraints and prior findings available to the work, so a reviewer and an AI teammate judge a change against the same facts. A good review record has three entries: what was accepted, what was changed, and what remains open. It does not need to capture every keystroke, only the facts that would otherwise be rediscovered during the next build or release check.

For the wider loop that connects building, evidence and release, read how Compass makes project knowledge reusable.

When is human review not enough on its own?

Human review is not enough when the volume of AI-generated changes outgrows the attention available. Three signs show it: pull requests are approved in seconds, reviewers stop opening the tests, or the same class of defect keeps returning. The fix is not more willpower. It is a smaller change size, stricter acceptance conditions and tests that carry more of the mechanical checking, so the reviewer can concentrate on intent.

Review should also grow with risk. A wording correction can use a quick check. A new customer workflow deserves explicit acceptance conditions, a tested path and a recorded release decision. Reviewing at the end of each meaningful slice of work, rather than after a large batch, keeps evidence easy to inspect while the context is still fresh.

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