How to review AI-generated pull requests with huge diffs

How should I review AI-generated pull requests with huge diffs

Review huge AI-generated pull requests in layers, not as one scroll through the diff. Start by deciding whether the change is reviewable at all, then check intent, tests, and risk areas first. Google’s code review guidance says changes should be small, and Microsoft’s playbook says large PRs should be split into clear chunks so a reviewer can understand them on their own.

The part people get wrong is treating the diff as the unit of review. The real unit is the behavior change. A huge AI-generated PR often mixes cleanup, refactors, generated code, and logic changes in one branch, which hides the thing you actually need to judge. GitHub’s review model is built around comments, approvals, and requests for changes on a pull request, but that only works well when the reviewer can connect the code to the intent.

Start with the PR description before opening files. Ask three questions: what problem does this solve, what user-visible behavior changes, and how was it tested. Microsoft’s guidance says reviewers need to know what the code does and should get actionable feedback, while Azure DevOps notes that a good review catches less-obvious problems, not just obvious bugs. If the description cannot explain the change clearly, the PR is not ready for a deep review.

Next, map the diff before reading line by line. Skim the file list, identify the few files that can break production, and separate them from mechanical edits. A generated migration, formatting pass, or dependency update usually deserves a different review path than business logic, auth, billing, or data access. GitHub supports CODEOWNERS and required reviews, which is useful here because the right reviewer should own the risky area instead of forcing one person to inspect everything.

Then review by risk, not by order. Open the files that can cause data loss, security issues, state corruption, or broken interfaces first. Check whether the PR changes validation, permissions, retries, idempotency, error handling, or persistence. Google’s reviewer guidance says technical facts and data outrank opinion, so when the change affects behavior, ask for tests or evidence rather than debating taste. If the author can justify a design with solid engineering principles, accept the choice.

Use the tests as a map to the real risk. For a large AI-generated PR, look for added tests that prove the new behavior and for existing tests that would fail if the change is wrong. Microsoft’s review guidance says reviews are not a replacement for tests, and the review should catch problems that tests do not catch. If the PR has no tests for the changed behavior, ask for them before you spend time on the deepest code paths.

When the diff is too big to understand, ask for a split instead of pretending to review it. Microsoft’s FAQ says to size work into small clear chunks so the reviewer can understand the code on its own, and Google’s guidance says reviewers can rightfully reject changes that are too large for a single review. That is not a courtesy issue, it is a correctness issue. A review you cannot finish is not a review.

A practical split is by behavior. One PR can handle the refactor, another can add the new API surface, and a third can wire up the UI or background job. If the author used AI to generate a broad patch, make them extract the smallest safe mergeable slice first. If a feature needs the whole bundle to work, ask for a feature flag or a temporary gate so the risky path stays off until the rest lands. Microsoft’s playbook explicitly recommends turning incomplete work into chunks that can be shipped safely.

For the files you do review, read in passes. First pass, check naming, boundaries, and whether the change matches the stated intent. Second pass, compare old and new control flow around edge cases. Third pass, inspect anything the AI tends to get wrong, such as null handling, duplicated logic, stale comments, unreachable branches, accidental broad catch blocks, and copy-pasted code that changed one line but not the surrounding assumptions. GitHub’s review tools let you comment on specific lines and suggest exact changes, which makes those targeted passes easier to record.

A concrete example helps. Suppose an AI-generated PR adds a payment retry helper, updates three call sites, and refactors a shared client. Do not start with formatting. Start by checking whether retries can duplicate charges, whether idempotency keys are preserved, whether failures are visible to callers, and whether the tests show both success and retry exhaustion. If the diff also cleans up variable names, ignore that until the behavior is proven safe.

When the PR is huge because the AI touched many unrelated files, say so plainly: the change is not reviewable as one unit. Ask for a rewrite, a smaller branch, or a series of PRs. The inconvenient part is that this can slow the merge, but merging an unreadable patch is worse. Google’s standard says nothing justifies checking in changes that worsen code health except an emergency, and Microsoft’s guidance says the review should protect important branches from unwanted changes.

If you are the reviewer and the author pushes back, move the discussion out of comment threads when needed. Google’s guidance recommends a direct conversation or video call when consensus is hard, then recording the outcome back on the change for future readers. That matters even more for AI-generated work, because the author may have accepted output that looks plausible but does not preserve the system’s real invariants.

Do not reward volume. A PR with five thousand changed lines is not more valuable than a PR with five well-chosen lines. The goal is to verify that the change improves the codebase and does not break the system. If the diff is noisy, ask the author to separate generated or mechanical edits from the actual product change, then review the meaningful part on its own. GitHub’s pull request workflow and Microsoft’s review guidance both assume the reviewer can understand the change they are approving.

If you want a rule you can use tomorrow, use this one: first validate intent, then isolate risk, then test the behavior, then split anything you cannot explain in one sentence. That sequence works better than trying to be heroic over a giant diff, and it keeps AI output from becoming a hidden merge risk. If your team already uses a place to exchange work and testers, DevConnect can fit that workflow at https://devconnectplatform.com, but the review standard stays the same: only approve what you can explain and defend.

FAQ

Should I approve a huge AI-generated PR if the tests pass

No. Passing tests are necessary, not sufficient. A large review still needs readable intent, clear scope, and a check that the changed behavior matches the author’s description. Azure DevOps explicitly says code review catches less-obvious problems that tests do not catch.

What if the AI PR contains a lot of formatting or generated code

Separate the mechanical part from the behavior change. Review the behavior first, then skim the mechanical edits for obvious breakage. If the mechanical portion obscures the real change, ask the author to split it out before merge. Microsoft’s playbook recommends small chunks the reviewer can understand on their own.

When is it reasonable to ask for a synchronous review call

Use a call when the same comments keep looping, when the change affects a sensitive area, or when the diff is hard to reason about in text alone. Google’s reviewer guidance says a face-to-face meeting or video conference can help resolve hard conflicts, then the result should be recorded on the change.

What should I comment on first in a giant PR

Comment on the part that can break users or data first: correctness, security, state changes, and regressions. After that, move to tests, then design and naming. GitHub reviews support line comments and review decisions, which helps you keep the important issues separate from polish.

Is it okay to reject an AI-generated PR because it is too large

Yes. If the patch cannot be reviewed responsibly as one unit, rejecting it until it is split is a valid code-quality decision. Google’s standard says the reviewer’s job is to improve code health, and Microsoft’s guidance says to size work into small clear chunks so the reviewer can understand it.