Reviewing AI-generated code is not the same as running an AI code-review tool. The model already wrote the diff. Your job is to decide whether that diff is true, complete, and safe to merge — which you cannot do if you cannot explain what the code does.
Human-written code usually leaves traces of how it was made: awkward names, a missing null check, a comment that admits uncertainty. Those traces tell a reviewer where to look. Model output is often fluent, consistent, and formatted as if it were finished. Fluency is not evidence that the code is correct. It is evidence that the generator is good at looking correct.
That matters because reviewers skim what looks polished. You skip building a mental model. You accept a helper that almost matches an existing one in the codebase. You miss that the function handles the happy path and silently drops the empty-input case. The code reads like a solution, so the review never becomes a test of understanding.
An Anthropic experiment with 52 early-career engineers, reported by InfoWorld in January 2026, found that people who used an in-editor model while learning a new Python library scored 17 percentage points lower on a quiz about the material they had just practiced (50% versus 67%). The widest gaps were in debugging and in seeing why code fails. Reviewing AI output is the same skill those quiz questions measured: can you still see what the code is doing when you did not write it.
Search results mix two different tasks. "AI code review" usually means a tool that comments on a pull request — CodeRabbit, Greptile, Copilot review, and similar products. Those tools analyze a diff and emit suggestions. "How to review AI-generated code" is the opposite direction: a human deciding whether to accept code a model wrote.
A bot that reviews PRs can catch mechanical issues. It will not hold your product invariants, your unwritten architecture, or the ticket you actually meant. ChatGPT can also "do a code review" if you paste a diff. That is another model commenting on the first model. Useful as a second pass. Not a substitute for a person who can explain the change to a teammate.
If you came here looking for a tool that reviews code automatically, that is a different article. This one is about the human pass you still have to do when the author is an LLM.
GitHub's Copilot tutorial on reviewing AI-generated code starts with functional checks: run automated tests and static analysis before you argue about the diff. That order is not optional. AI-generated code is unusually good at looking right in a file view and unusually willing to break on empty lists, nulls, and the one input the prompt never mentioned.
If the same model also wrote the tests, those tests are not independent evidence. They often encode the same wrong assumption as the implementation. Treat generated tests as a description of what the model believed, not as proof. Prefer tests that already existed, tests you wrote, or a manual reproduction of the ticket.
A useful first question is not "does this look clean?" It is "what command would fail if this were wrong?" If you cannot name that command — a test, a request, a job, a UI path — you are not reviewing yet. You are reading.
Before you approve, write a short explanation of the change as if you had to hand it to another developer. What enters. What leaves. What happens on empty input. What the code does not handle. Five to ten sentences is enough. If you cannot write that without looking back at the model's comments, you do not understand the change well enough to merge it.
This is slower than skimming. It is faster than a production incident caused by a fluent helper that renamed a field and stopped writing a downstream column. Explanation forces the mental model that fluency lets you skip. It also surfaces the usual AI miss: the code does something adjacent to the ticket, not the ticket.
You do not need a special tool for this step. A PR description you wrote yourself, a comment you could defend in standup, or a paragraph in a notes file all work. The test is whether the explanation would still be true if you deleted the model's prose and kept only the code.
GitHub lists failure modes that show up more often in model output than in human drafts: hallucinated APIs, ignored constraints from the prompt, logic that looks right but does not match intent, and tests that were deleted or skipped instead of fixed. Check those on purpose. A human author who cannot get a test green usually leaves the failing test in the diff. A model will sometimes remove the test.
New dependencies need a separate pass. Confirm the package exists, is the one you think it is, and is maintained. Hallucinated package names are a documented risk in GitHub's own guidance. Do not install a library because the import compiled in the model's story. Install it because you found it.
Also look for duplication against the codebase you already have. Models invent a utility that is 80% of a function two directories away. That is not a style nit. It is a second source of truth that will drift. Search for the behavior before you accept a new helper.
If you merge it, you own it. That is true for a junior's PR and it is true for Copilot. "The model wrote it" is not an incident review. The practical rule is the same one senior engineers already use on human PRs they do not understand: do not approve. Ask, or rewrite until you can explain it, or reject.
This is also how you keep the skill the Anthropic study was measuring. If every unclear block is accepted because it compiled, you stop practicing the part of the job that still belongs to you: seeing why code fails. Reviewing AI-generated code is a reading skill. Generation speed does not replace it.
A clean test for yourself: could you debug this at 2 a.m. without the original prompt? If the answer is no, the review is not done.
Use a fixed order so fluency does not set the agenda. First: run existing tests, linters, and the path the ticket describes. Second: check intent — does this change the thing the ticket asked for, in this architecture, with these names. Third: write the short explanation. Fourth: hunt AI-specific misses — fake APIs, new packages, deleted tests, cloned helpers. Fifth: style, and only then.
If a step fails, stop. Do not "keep reading to see if it makes sense later." Later is how a polished wrong function lands. Send it back, regenerate with a tighter constraint, or write the missing piece yourself.
The bottleneck in 2026 is not typing the code. It is being able to read code you did not write and still tell the truth about it. That is the review.