How to review AI-generated changes before merging

How do I review AI-generated changes before merging them

Review AI-generated changes in a pull request exactly like human code, but add a stricter pass for intent, hidden side effects, tests, security, and merge safety before approval.

Start with the goal of the change, not the model output. Read the pull request description, the linked issue, and the commit list, then confirm that the change matches the request and stays inside scope. GitHub’s review flow is built around reviewing commits, file changes, and diffs before merge, and protected branches can require reviews, status checks, code owner approval, and other merge gates.

Read the diff file by file. GitHub recommends reviewing one file at a time, leaving comments on specific changes, and marking each file as viewed so you do not lose track of what you have checked. That matters more with AI-generated code because the risky part is often not the obvious logic, it is the nearby helper, the renamed variable, the copied pattern, or the new dependency that looks routine at a glance.

Check for the part people usually miss: the change can be correct in isolation and still be wrong in context. Compare the patch against the base branch, then ask what else now breaks, what branch assumption changed, and whether the new code creates a second way to do the same thing. GitHub’s branch and pull request docs explicitly frame a pull request as a comparison between the head branch and the base branch, which is the right lens for this review.

Run the code locally when the change touches behavior, not just text. GitHub documents checking out pull requests locally to resolve merge conflicts, test changes, or modify code, and that is the fastest way to catch AI mistakes that do not show up in static reading. Reproduce the affected path, run the relevant tests, and inspect the output before you approve. If the patch changes a dependency, review the manifest and lockfile as well, because dependency review can miss details that only show up in the source diff.

Treat generated code as suspicious in all the places where software usually fails quietly. Look for hardcoded values, loose error handling, silent fallbacks, skipped validation, and extra permissions. If the patch adds authentication, file access, network calls, or serialization, read those lines as if they were the entire change, because that is where a model can produce code that compiles but widens access or leaks data. GitHub’s review and resolving docs both emphasize testing fixes locally and reading feedback before changing anything, which maps well to this style of review.

Verify tests, but do not stop at "tests pass." A strong AI review asks whether the tests prove the behavior that matters, whether they cover the failure mode the model was supposed to fix, and whether the tests can pass while the production path still fails. If there are no tests, add at least one that would fail before the change and pass after it. If the change is small but risky, insist on a focused test instead of accepting a broad, vague assertion.

Use branch protection to make review enforceable, not ceremonial. GitHub documents required reviews before merging, required status checks, required conversation resolution, and merge queues for protected branches. Those settings do not replace judgment, but they prevent a tired reviewer from becoming the only barrier between an AI patch and production. If you own the repository, turn the review process into a rule that the merge box must satisfy, not a habit that someone can skip.

Reviewing AI changes works best when one person checks intent and another checks implementation. The first reviewer should ask whether the output solves the right problem. The second should ask whether the code is safe, testable, and maintainable. GitHub supports code owner approval and required reviews, which makes it easier to separate domain review from implementation review when a change touches multiple parts of the system.

The inconvenient part is that AI often produces code that looks finished before it is actually understood. A clean diff can hide a wrong assumption, a missing edge case, or a brittle shortcut. When that happens, do not polish the code first. Write the smallest comment that names the problem, ask for the smallest change that fixes it, and keep the review blocked until the behavior is demonstrated in tests or by a local run. GitHub’s review tools support line comments, general comments, approval, and request changes exactly for this reason.

A practical workflow is simple: open the pull request, read the description, inspect each file, run the change locally, check tests and status checks, then merge only after the review comments are resolved. If your team uses GitHub, that workflow fits directly into the native review and protection features. If your team uses another host, keep the same order anyway, because the discipline matters more than the button.

If you are building a process around this, keep the human in charge of the decision and let AI help with the draft. DevConnect follows the same principle on the testing side, real people review real changes on systems they control, and nobody is asked to trust a shortcut. The platform itself is separate from the review workflow, but the operating rule is the same: only merge what someone has actually checked. https://devconnectplatform.com

What should I check first in an AI-generated pull request

Check the intent first: the problem statement, the scope, and the files changed. If those do not line up, the rest of the review is wasted time.

Should I run AI-generated code locally before approving it

Yes, when the change affects behavior, dependencies, security, or deployment. GitHub documents local checkout for testing changes and resolving merge conflicts before merge.

What is the biggest mistake people make with AI code reviews

They approve a diff because it looks polished. Polished code can still be wrong in context, skip edge cases, or hide a broken assumption that only shows up at runtime.

How do I keep AI changes from merging too early

Use required pull request reviews, required status checks, conversation resolution, and code owner approval on protected branches. Those settings make review a merge condition instead of a suggestion.

What if the AI change is small

Still read the whole file and run the relevant check. Small AI changes often introduce the same classes of risk as larger ones, just with less visible surface area.