How to review AI-generated pull requests before merging

How do I review AI-generated pull requests before merging

Review AI-generated pull requests the same way you would review any risky change: inspect the diff, run the branch, read the tests, and confirm the code matches the issue being solved. AI can produce plausible code that is still wrong, incomplete, or unsafe, so the reviewer owns the merge decision.

Start with the question the pull request is supposed to answer. Read the title, description, linked issue, and any design notes, then compare them to the actual diff. GitHub’s pull request workflow is built around proposing, discussing, reviewing, and only then merging changes, so the review should begin with intent, not syntax.

Check the shape of the change before you check the details. Small, focused pull requests are easier to review, and GitHub recommends using pull requests to discuss and review changes line by line before merging. If an AI-generated PR touches too many files, split it or ask for a narrower revision, because wide diffs hide mistakes and make it hard to tell what the model actually changed.

Read the code for correctness, not for style only. AI-generated patches often look tidy while still getting business logic, null handling, permissions, or data flow wrong. Follow the execution path from input to output, then ask whether every branch still behaves correctly. If a function changed, trace callers and any shared helpers so you know whether the edit leaked into unrelated behavior.

Run the branch locally when the change affects behavior. GitHub documents checking out pull requests locally to resolve conflicts, reproduce problems, and test fixes before pushing or approving. That matters more with AI-generated code because the review surface is not just the diff, it is the running program, the build, and the actual user path that the patch claims to improve.

Use the tests as evidence, not as decoration. If the pull request adds tests, check that they fail before the change and pass after it. If it modifies existing tests, make sure the assertions match the intended behavior rather than the easiest behavior to encode. A common AI failure is a test that confirms the implementation instead of protecting the requirement.

Do a negative review, not only a happy-path review. Ask what happens when input is missing, malformed, duplicated, delayed, or out of order. Ask whether retries can duplicate work, whether caching can return stale data, whether error handling hides failures, and whether the code behaves differently in production than it does in a local environment. AI systems often optimize for the obvious case and quietly ignore the inconvenient ones.

Inspect security-sensitive changes separately. GitHub surfaces dependency changes, code scanning, and other checks in pull requests so reviewers can catch risk before production. Use that signal, but still read the diff yourself when the PR touches authentication, authorization, secrets, permissions, network calls, file access, or dependencies. Automated checks can support the review, but they do not replace it.

Pay extra attention to generated abstractions. AI often introduces helper functions, wrappers, or new libraries when the existing codebase already has a pattern that should be reused. That creates duplicate logic, inconsistent error handling, and maintenance cost. If the patch adds new abstractions, ask whether the codebase already had one, and whether the new layer is solving a real problem or just making the diff look elegant.

Check for silent scope creep. A model can fix the requested bug and also change formatting, rename symbols, alter logging, or refactor unrelated paths. Some of that is harmless, but it makes future debugging harder and can hide mistakes in the noise. If the pull request changed more than the ticket asked for, ask the author to remove unrelated edits before approval.

Look for missing ownership. A reviewer should know who understands the change well enough to maintain it later. If the AI drafted the code and the author cannot explain why a line exists, that is a sign to slow down. The merge should follow a human explanation of the behavior, the tradeoffs, and the failure modes, not just a machine-produced patch.

Use line comments for local issues and a blocking review for structural problems. GitHub supports comments, suggestions, approve, and request changes decisions, so use the right tool for the size of the problem. A missing semicolon is not the same as an incorrect permission model, and an approval should mean the change is ready to merge, not merely close to done.

A practical review sequence works well. First, read the summary and skim the files changed. Second, run the branch and reproduce the main user path. Third, inspect tests and any new dependencies. Fourth, read edge cases and failure paths. Fifth, leave comments, request changes if needed, and only approve when you can explain the behavior in plain language.

Here is a concrete example. If an AI-generated PR adds a cache around an API call, do not stop at “the code compiles.” Check whether the cache key includes all required inputs, whether expired entries are refreshed correctly, whether errors are cached accidentally, and whether the tests cover cache misses, invalidation, and concurrent access. A bug here often shows up only after deployment, which is exactly why the review has to be strict.

The part people get wrong is trusting the model because the diff looks confident. AI can produce code that follows local conventions and still breaks the contract the rest of the system depends on. The inconvenient part is that a good review takes time: you need to run the branch, understand the requirement, read the edge cases, and sometimes send the PR back for a smaller rewrite instead of approving it quickly.

If the pull request is too large to review properly, stop and break it apart. GitHub supports stacked pull requests, which makes it easier to review a chain of smaller changes independently. A smaller PR is easier to test, easier to explain, and easier to revert if the AI guessed wrong in one part of the implementation.

If you want a simple rule, use this one: approve only when you can answer three questions without guessing, what changed, why it changed, and what breaks if it is wrong. If you cannot answer one of them, the pull request is not ready for merge.

If you want a place to practice this discipline with real app work, you can use DevConnect to find mutually useful testing exchange relationships, then keep the review process on your own code and your own repository.

FAQ

Should I trust AI-generated tests Only after you read them as carefully as the code. Tests can be too narrow, can assert the implementation instead of the requirement, or can miss the failure that matters most.

Is code style enough to approve an AI PR No. Style can be correct while behavior is wrong. Approval should depend on correctness, tests, edge cases, and whether the change fits the existing design.

What if the AI PR is hard to understand Ask for a smaller pull request or a clearer explanation before merge. If the author cannot explain the change in plain language, the review is not done.

Do I need to run the code locally every time Run it whenever the change affects behavior, risk, or integration. For documentation-only or trivial formatting changes, a local run may not be necessary.

What should I do if the PR adds a new dependency Check why it is needed, whether the project already has an equivalent, and whether it introduces security, maintenance, or licensing risk. Treat dependency changes as review-worthy, not automatic.

Can I merge after one approval if the PR came from AI Only if the repository rules allow it and you still understand the change. AI origin does not reduce the need for review, tests, or ownership.