How to Review AI-Generated Code Before Merging

How can I review AI-generated code before merging it

Review AI-generated code the same way you review any other pull request, then add one extra layer of skepticism. Read the diff, understand the intent, run the branch locally, check the tests, and make sure the change is safe to keep six months from now.

Start with the reason the change exists. A good review begins with the problem statement, not the generated code. GitLab’s merge request guidance says to understand why the change is necessary, then review for quality, security, reliability, and maintainability. GitHub also recommends reading review feedback carefully, checking out the branch locally, and addressing security findings before merge.

Skim the whole diff before reading line by line. You want the shape of the change first: what files moved, what behavior changed, what data now flows through the system, and whether the AI touched more surface area than the task needed. AI-generated patches often look clean at the line level while making a broad, accidental design change across multiple files.

Check the code for correctness, not just style. Ask whether the new logic handles nulls, empty input, retries, partial failure, timeouts, and duplicate events. A generated function can look polished and still miss one branch that only appears in production traffic. If you cannot explain what every branch does, the code is not ready to merge.

Read the tests with the same care as the implementation. Tests should prove the behavior that matters, not only the happy path that the model guessed first. GitLab’s review checklist explicitly calls for automated tests, and GitHub’s review guidance points reviewers to local reproduction and test fixes when feedback raises a problem. If the patch changes behavior without changing tests, treat that as unfinished work.

Run the branch locally, even when the diff looks obvious. A model can generate code that compiles and still fails because of environment assumptions, framework version drift, missing fixtures, or an API response that differs from the prompt. If the change is UI related, open the interface. If it touches an endpoint, hit the endpoint. If it affects a job, run the job. GitLab’s review flow explicitly tells reviewers to test the code.

Look for hidden scope expansion. AI often adds helper functions, new abstractions, new dependencies, or a refactor that is larger than the request. That extra code is where review gets expensive. A small task should not introduce a new architecture, a new library, or a new abstraction unless the change request truly needs it. Simpler code is easier to verify, easier to revert, and easier to maintain.

Review security with a separate pass. Check for unsafe parsing, unsanitized input, secret exposure, overbroad permissions, and places where model output becomes execution. GitHub’s security guidance highlights dependency review and code scanning in pull requests, and its AI review guidance calls out least-privilege permissions and separating analysis from execution for production-sensitive workflows. A generated patch can be correct and still be unsafe.

Pay attention to dependencies and build changes. AI-generated code often reaches for a new package instead of using what is already in the repo. Every new dependency creates update work, license review, and security review. If the same result is possible with existing code, prefer that path. If a dependency is necessary, confirm the change is deliberate and justified in the review thread.

Check observability and rollback behavior. A change is easier to trust when it leaves a trace in logs, metrics, or error reporting that helps you see what happens after merge. If the patch changes a critical path and there is no way to tell whether it works, the review is incomplete. The inconvenient part is that this forces you to think about failure after merge, not only success in the branch.

Use a checklist for the parts people forget. Ask whether the code is backward compatible, whether there are migration steps, whether any data shape changed, whether retries can duplicate work, and whether the change is safe under load. GitLab’s checklist explicitly calls out backward compatibility, varied existing data, frontend and backend impact, and flaky tests tied to the merge request.

Do not approve code you cannot explain. If a model wrote a clever expression and nobody on the team can explain why it works, the code is not reviewable yet. Ask for comments, a smaller diff, or a rewrite that makes the intent obvious. GitLab’s review guidance says to say when you do not understand a piece of code. That is not hesitation, it is the point of review.

A practical review order works well: first read the problem statement, then skim the diff, then inspect the risky files, then run tests locally, then check security and dependencies, then verify observability and rollback, then leave comments. GitHub and GitLab both describe review as a sequence of understanding, testing, and resolving issues before merge, not a single pass over the code.

The part people get wrong is trusting surface quality. AI-generated code often reads well, uses neat naming, and looks confident. That does not mean it is correct, minimal, or safe. The review has to answer one plain question: if this ships, what breaks first, and would we know quickly enough to fix it

The part that is inconvenient is that good review takes almost as long as writing the patch. If the change is important, you need to reproduce it, check alternate inputs, inspect the tests, and verify the security impact. That time is cheaper than debugging a bad merge later. DevConnect follows the same principle in practice, because the people who test code should be the people who can show real work on owned projects, and you can see that approach on devconnectplatform.com.

A concrete example helps. Suppose an AI tool adds retry logic to an API client. A shallow review checks that the code retries. A better review checks whether retries happen on the right errors, whether the delay can explode traffic, whether the client logs each attempt, whether idempotent calls stay safe, and whether tests cover a permanent failure plus one transient failure. That second pass catches the bug before users do.

If the review exposes a problem, ask for the smallest fix that proves the behavior. A narrow patch is easier to validate than a rewrite. If the AI produced a clever but opaque solution, ask the author to replace it with straightforward code and tests. Review is not about rewarding the model, it is about merging code you can defend.

For teams, the strongest guardrail is a merge rule that requires both human review and passing checks. GitHub’s pull request guidance emphasizes review, testing, and security checks before merge, and GitLab’s guidance makes clear that unresolved warnings, failed jobs, and missing approval stop a merge request from being ready. Put the burden on the process, not on memory.

If you want a simple standard, use this: understand the change, reproduce it, challenge the edge cases, check the tests, check the security impact, and only then approve. AI can draft code fast. Review is the work that decides whether the code is real.