How is reviewing AI-generated code different from normal code review?

AI-generated code is uniformly tidy, so the usual signal — messy code marks risky code — disappears. Review has to target scope creep, invented APIs, silently swallowed errors and tests written from the implementation rather than the requirement.

What should you check first in an AI-generated pull request?

The file list, before the line diff. Models often change more than they were asked to, and unrelated reformatting or renames hide the few lines that actually matter.

Can AI-generated tests be trusted?

Not on their own. Tests generated alongside the code are derived from that code, so they can pass while the feature is wrong. Read each assertion and ask what user-visible behaviour would have to break for it to fail.

How do you review AI-generated code when you are building solo?

Keep each change small enough to read, ask the model what could break and verify two or three of those claims against the code, and click the flow — including the error path — before merging.

How to Review AI-Generated Code Before You Merge It

To review AI-generated code, check it against the intent you actually had rather than the prompt the model answered, verify that every import, API and dependency it references really exists, and execute the paths the diff touches before you trust them. AI-generated code is usually well-formatted, confidently commented and syntactically clean, which is exactly why review habits tuned to catching sloppy human code miss its failure modes: silent behaviour changes in untouched paths, invented APIs, swallowed errors and edge cases nobody ever ran.

Why does AI-generated code need a different review checklist?

Human-written code tends to fail where the author was tired, rushed or unfamiliar with the codebase — and it usually looks the part. Reviewers learn to slow down around messy diffs. AI-generated code inverts that signal. It arrives uniformly tidy, with docstrings and sensible variable names, whether the logic underneath is correct or invented.

The second difference is scope. Ask a model to "fix the checkout bug" and it may also reformat a utility file, rename a parameter three call sites away, or quietly change a default. The diff is larger than the intent, and the extra lines are the ones nobody reads carefully.

The third is confidence. A model does not signal uncertainty the way a colleague does. There is no "I wasn't sure about this bit" comment on the pull request, so the reviewer has no map of where the risk is concentrated.

Review focus	Human-written diff	AI-generated diff
Where bugs cluster	Complex or rushed sections	Anywhere — surface quality is uniform
Scope	Usually matches the ticket	Often wider than asked; check untouched-looking files
Dependencies	Author knows what's installed	May reference packages or methods that don't exist
Error handling	Omitted visibly	Present but may swallow failures silently
Tests	Written for real behaviour	May be written to match the code, not the requirement

How to review AI-generated code in six passes

1. Re-read the requirement before you read the diff

Write down, in one sentence, what the change is supposed to do. Then read the diff and ask whether it does that and only that. This ordering matters: if you read the code first, you will start evaluating whether it is good code instead of whether it is the right code. Most AI review misses happen here — the implementation is competent and solves a slightly different problem.

2. Check the scope of the diff

List the files touched. For each one, ask why it is in this change. Formatting churn, renamed internals and "while I was here" refactors should be split out or reverted — not because they are wrong, but because they hide the three lines that matter.

3. Verify every external reference

Open the docs for each unfamiliar import, method signature and config key. Models can generate plausible names for things that do not exist, or use an API shape from a different major version — a failure mode worth understanding in detail in our write-up on AI code hallucinations. A fabricated package name that nobody notices is also a supply-chain risk, because the name can be registered later by someone else.

4. Walk the unhappy paths

AI-generated code reliably handles the case described in the prompt. Ask what happens on an empty list, a null user, a 500 from the API, a duplicate submit, a slow network, an expired token. Then look for the opposite problem: a try/except or catch that logs nothing and returns a default, turning a loud failure into a silent wrong answer.

5. Check the data and security boundary

Three questions catch most of it. Does this change touch authentication, authorisation or a permission check? Does it move user data anywhere new — a log line, a third-party call, a client-side bundle? Does any user input reach a query, a file path, a shell command or a template without validation? Generated code frequently assumes the caller already validated the input.

6. Read the tests as evidence, not as a checkbox

Tests generated alongside the code are written from the code, which means they can pass while the requirement fails. Read each assertion and ask what behaviour would have to break for it to fail. If the answer is "nothing a user would notice", the test is decorative. Our guide on testing AI-generated code covers how to build a suite that actually constrains behaviour.

What failure modes slip past a normal review?

Failure mode	What it looks like in the diff	How to catch it
Invented API or package	A clean, readable call to something that doesn't exist	Open the docs; run the install and the code path
Silent error swallowing	A catch block returning a default or empty array	Grep the diff for empty catch bodies and bare defaults
Scope creep	Unrelated files reformatted or renamed	Review the file list before the line diff
Behaviour change in an untouched path	A shared helper or default value quietly altered	Find the callers of anything the diff modifies
Tautological tests	Assertions that restate the implementation	Mutate the code and confirm the test fails
Missing state transitions	Happy path only; no loading, empty or error UI	Click the flow in a browser, not just read it

How much of the review can you automate?

Static analysis, type checking, linting and a dependency audit should run before a human ever opens the pull request — they catch the mechanical half cheaply and consistently. What they cannot tell you is whether the feature behaves correctly for a real person, which is where most AI-generated bugs actually live.

That second half is what Klavity Sims is for: AI personas exercise the flows your change touches the way a user would — including the empty, slow and error states a generated test suite usually skips — and report what broke with the evidence attached. AutoSim turns the flows that matter into self-healing end-to-end tests, so the same paths are re-verified on every subsequent AI-written change instead of being reviewed from scratch each time. For the wider picture of where automated QA fits around human review, see our complete guide to AI QA.

What does this look like for a vibe-coded app with no reviewer?

If you are building solo with Cursor, Bolt, Lovable, v0 or Replit, there is no second pair of eyes — so the review has to become a routine you run against yourself. Three habits carry most of the weight:

Keep changes small enough to read. One feature per prompt, one prompt per commit. A 600-line generated diff will not get reviewed, by you or anyone.
Make the model explain, then verify the explanation. Ask what the change does and what could break. Then check two or three of those claims against the actual code. Disagreements are where the bugs are.
Click the flow before you merge. Reading generated code gives a false sense of coverage. Running it — including the error path — is the only pass that tests reality.

Pair that with a way to capture what you find. When a bug does surface, Snap turns a right-click into a report carrying the console errors, network calls and the exact state of the page, so the fix prompt you write next has the context the model needs instead of a vague description.

What should you confirm before you merge?

The change does what the requirement says, and nothing else.
Every import, API and config key has been verified against real documentation.
The unhappy paths were executed, not just read.
No error is caught and silently discarded.
Nothing crossed the auth, permissions or user-data boundary without being looked at directly.
At least one test would fail if the feature regressed.

Review is the cheapest place to catch an AI-generated bug — cheaper than QA, far cheaper than a client finding it. But review alone cannot cover behaviour, which is why the fastest teams pair a short, disciplined reading of the diff with automated verification of the flows it touches.

Try Klavity free — point it at your next AI-generated change and see what the review missed.