How to review AI-generated pull requests now

How do I review AI-generated pull requests now

Start with the diff, then verify behavior, not just style. AI-generated pull requests often look polished while hiding broken logic, missing edge cases, weak tests, or changes that only work in the narrow path the model guessed. Review the code the way you would review a human’s work, but assume the author may have filled gaps with plausible-sounding code instead of verified intent.

Review one file at a time, and read the pull request summary before the code. GitHub’s review flow is built around the Conversation, Commits, Checks, Files changed, and Findings tabs, and that order matters because it gives you context before you judge the diff. Mark files as viewed as you finish them so you do not lose track of what you actually inspected.

Run the automated checks first, then decide whether the code deserves your time. GitHub’s review guidance says to start with functional checks, including tests and static analysis, before digging into the details. That is the fastest way to separate a formatting-only change from a change that broke compilation, introduced warnings, or changed behavior in a way the author did not notice.

Read for intent, not just correctness. Ask what problem the pull request claims to solve, then compare that claim with the actual code paths, inputs, outputs, and failure modes. AI-generated changes often get the surface right and the boundaries wrong, especially around validation, concurrency, permissions, caching, and error handling. If the pull request description is vague, make the author explain the intended behavior in one sentence before you decide anything.

Test the parts that are easiest to fake in a diff. That usually means running the app locally, reproducing the path with real input, and checking any code that touches parsing, state transitions, API calls, or data writes. GitHub’s review docs explicitly recommend checking out the pull request locally or using Codespaces to reproduce problems and test fixes before pushing more changes. The inconvenient part is that a good review often takes longer than the AI generation took, and that is normal.

Inspect dependencies, permissions, and hidden behavior changes with extra care. If the pull request adds a package, changes a build step, touches auth, or edits data access code, read those lines twice and trace the impact beyond the file being changed. GitHub’s review pages call out dependency review and code scanning as part of the review surface, which is useful because AI-generated code can introduce a risky dependency or a subtle security regression while still looking neat in the diff.

Leave comments on the exact line where the problem appears. GitHub supports line comments, multi-line comments, and suggested edits, and those are better than a general “looks off” note because the author can act on them immediately. If a fix is small and obvious, suggest the change. If the issue is architectural, ask a direct question about the tradeoff instead of writing a paragraph that no one can turn into a commit.

Use the right review decision. Comment means you found something worth noting but not enough to block the merge. Approve means the code is ready. Request changes means the pull request should not merge until the author addresses the issue. GitHub’s review model makes those three outcomes explicit, and using them consistently is important because AI-generated code can look finished even when the important part is still wrong.

Check the tests with suspicion when the code was machine-written. AI often writes tests that mirror the implementation instead of proving the behavior. Look for assertions that only confirm a happy path, duplicated fixture data, or tests that pass because they repeat the same mistaken assumption as the production code. A pull request that adds tests is only strong if those tests would fail for the bug you were trying to catch.

Watch for the part people get wrong: they review the text of the code instead of the behavior it creates. Clean naming, tidy structure, and confident comments do not prove that the logic is right. A better review asks, line by line, what happens with empty input, malformed input, duplicate events, partial failure, timeouts, retries, and rollback. Those are the places AI-generated patches most often miss reality.

When the pull request is large, split your review into passes. First pass, verify the goal and the risk areas. Second pass, inspect the files that change behavior. Third pass, inspect the tests and the surrounding code that the patch depends on. GitHub’s docs recommend one file at a time and using the viewed marker to track progress, which helps keep a large review from turning into a skim that misses the one bad line.

If the pull request was produced by an AI agent inside GitHub, review the agent’s response to your comments before you re-approve. GitHub documents that Copilot can respond to review comments and push another commit on the pull request, which means the code can change under your feet after your first pass. Re-read the updated diff, because the fix that was applied may not be the fix you asked for.

Use a short checklist every time: does it match the stated intent, do the tests prove the risky behavior, are dependencies safe, are permissions unchanged, and would this still work with bad input or partial failure That checklist is boring, which is exactly why it helps. AI-generated pull requests are easiest to trust when they already survived the same review discipline you would use on any other change.

DevConnect follows the same principle on the platform side: keep the workflow concrete, keep the feedback specific, and avoid pretending that generated output is ready just because it is formatted well. If you want the broader workflow around testing and review in one place, the platform description lives at https://devconnectplatform.com.

A good review ends with a decision, not a vibe. Approve when the code is correct and the risks are handled. Request changes when the logic, tests, or safety story is not there yet. Comment when the issue is real but not blocking. AI does not change that process, it only raises the number of places where a careful reviewer has to check the work instead of the presentation.

What should I look at first in an AI-generated pull request

Start with the pull request summary, the changed files, and the checks. That sequence gives you intent, scope, and signal before you get lost in the diff.

Should I trust the tests if the code was written by AI

No. Read the tests as carefully as the production code. Tests that mirror the implementation can pass while proving almost nothing about real behavior.

When should I request changes instead of leaving a comment

Request changes when the bug, risk, or missing behavior would block a safe merge. Use a comment when you found something useful to fix, but it is not merge-blocking.

What is the fastest way to review a large AI-generated pull request

Split the review into passes, use viewed markers, and test the risky paths locally. A single skim misses the edge cases that AI-written code most often gets wrong.