Do I still need to test AI-generated code if it runs without errors?

Yes. Running without errors only proves the code is syntactically valid and that the happy path executes. AI generators produce code that satisfies the prompt, not code that is consistent with the rest of your app, so the failures show up at integration points — a missing auth check, state that does not survive a reload, an empty list that renders a blank screen instead of a message.

Can I just ask the AI to write tests for the code it generated?

Not as your only safety net. If the model misunderstood the requirement, it will encode the same misunderstanding in the test, and the test will pass against a broken implementation. AI-written tests are useful for coverage of code you have already reviewed; they are not a substitute for a check written from the requirement rather than from the code.

What should I test first in an AI-generated feature?

Authorization on every new endpoint or query, data that must persist across a page reload, error and empty states, anything touching money or user data, and whether secrets ended up in the client bundle. These are the areas where a generated implementation most often looks correct and behaves incorrectly.

How do I test code I do not fully understand?

Test it from the outside. Write down what the feature should do in plain language, then verify each statement through the UI or the API as a user would experience it. Black-box behavioral checks do not require you to understand the implementation, and they stay valid when the AI rewrites that implementation next week.

Testing AI-Generated Code: A Practical QA Playbook

Testing AI-generated code means verifying behavior you never designed, so the instinct that normally tells you where bugs hide does not apply. The practical approach is to test at the seams rather than at the function level: check authorization on every new endpoint, state that must survive a reload, error and empty states, and anything touching money or user data. Treat the prompt you wrote as the acceptance spec, verify each statement from the outside as a user would experience it, and never let the same model that generated the code be the only thing that tests it.

Why does testing AI-generated code need a different approach?

When you write code yourself, you carry a map of it. You know which branch you rushed, which helper you copied from elsewhere, which assumption you made about the shape of the data. That map is what makes normal testing efficient — you test the parts you already suspect.

With generated code, the map does not exist. The model produced something that satisfies the prompt in isolation, and it produced it with total confidence and no memory of the rest of your application. So the defects cluster differently. A generated function is usually internally coherent; what it gets wrong is how it connects to everything around it. That is why unit-testing generated functions one at a time feels productive and catches almost nothing.

The second difference is churn. AI-assisted code gets regenerated, not edited. Ask for a change and you often get a new implementation rather than a patch, which means any test coupled to internal structure — mocked private calls, asserted call order, snapshot of an internal shape — breaks on a change that did not break the feature. Tests for generated code need to be written against behavior, because behavior is the only thing that stays still.

What should you check first in AI-generated code?

Five areas account for most of what slips through. Check them in this order, because they are ranked by how badly they fail in production versus how convincing they look in review.

Area	What generated code typically gets wrong	How to check it in a minute
Authorization	The endpoint or query checks that a user is logged in, but not that this user owns the record	Log in as a second account and request the first account's record by ID
Persistence	State lives in component memory; it looks saved until the page reloads	Do the action, hard-refresh, confirm it is still there
Empty and error states	The happy path is rendered; zero results or a failed request render a blank screen	Load the view with no data, then with the network throttled or offline
Secrets and keys	An API key sits in client-side code because the prompt did not specify a server call	Search the built bundle for the key prefix; check the network tab for third-party calls from the browser
Money and destructive actions	Amounts computed in floating point, or a delete with no confirmation and no ownership check	Run one transaction end to end and read the stored value, not the displayed one

None of these require you to read the implementation. That is the point — they are checks you can run against a feature whose code you have not internalized, which is the situation you are actually in.

How do you test code you did not write and do not fully understand?

Test it from the outside, and let your prompt be the specification.

The prompt you typed is the closest thing to a requirements document that exists for this feature. Open it and split it into individual claims: users can upload a file, only images under 5 MB are accepted, the upload appears in their library immediately. Each claim is now an acceptance check. Walk through them in the running app, one at a time, and mark each as verified or not. This takes minutes, requires no knowledge of the implementation, and reliably surfaces the requirement the model quietly dropped — usually the constraint in the middle of a long sentence.

Then extend each claim by one step the prompt did not mention. If images under 5 MB are accepted, what happens at 50 MB — a clear message, or a silent failure? If the upload appears in the library, does it still appear after a reload? Generated code is almost always complete on the path described and thin one step off it, so the step off the path is where you spend your time.

This is also the discipline we cover in depth in our guide on how to QA a vibe-coded app, and the specific failure patterns are catalogued in common vibe coding mistakes.

Why shouldn't the AI write its own tests?

Asking the model that wrote a feature to also write the tests for it produces tests that are guaranteed to pass and prove nothing. The reason is structural rather than a matter of model quality: the tests are derived from the implementation, so any misreading of the requirement is present on both sides of the comparison. The test asserts that the code does what the code does.

You can see this most clearly when the requirement was misunderstood. If the model decided that "archive" means delete, it will write a test asserting that archiving removes the record — and that test will pass, in CI, in green, forever, while your users lose data.

Generated tests still have a place. They are good at filling in coverage for code you have already read and confirmed, at exercising input permutations you would not bother to type, and at producing fixtures. The rule that keeps them honest is one of independence: the thing that defines correct behavior must come from outside the code. Either you write the assertion from the requirement, or you generate the test from the requirement in a separate session with no sight of the implementation.

What does a practical test loop for AI-generated code look like?

You do not need a full test pyramid on day one. You need layered checks that each catch a different class of failure, in rough order of cost.

Layer	Catches	Effort
Prompt-as-spec walkthrough	Dropped requirements, misread constraints	Minutes, manual, do it every time
Seam checklist (the five areas above)	Auth holes, lost state, blank error screens, leaked keys	Minutes, manual, do it before any client sees it
AI persona runs	Real-user paths nobody scripted — the wrong order, the back button, the half-filled form	Automated; this is what Sims does
Behavioral E2E on critical journeys	Regressions when the AI regenerates the feature next week	Automated and self-healing with AutoSim, so selector churn does not break the suite
In-app bug capture	Everything the above missed, with reproduction context attached	Snap — right-click, report, console and network included

The ordering matters more than the completeness. The top two layers cost almost nothing and catch the defects that embarrass you in front of a client; the automated layers stop those defects from coming back the next time the code is regenerated. For the broader picture of how these fit together, see our complete guide to AI QA.

How do you keep tests from rotting when the AI rewrites the code?

Three habits do most of the work. First, assert on user-visible outcomes — text on screen, a record in the database, a status code — never on internal call sequences. Second, select elements by role, label, or a stable test ID rather than by generated class names, which change on every regeneration. Third, when a test fails after a regeneration, resist the urge to update the assertion until you have confirmed the new behavior is actually correct; a rewritten implementation that quietly changed behavior looks identical to a stale test, and treating one as the other is how silent regressions ship.

Self-healing E2E exists precisely because selector churn is the dominant maintenance cost here. But no amount of healing distinguishes an intentional change from a regression — that judgement stays with you, and it is why the prompt-as-spec habit is worth keeping even after automation is in place.

Start testing your AI-generated code today

Pick your most recently generated feature and run the five-area seam check on it right now. It takes under ten minutes, needs no knowledge of the implementation, and those five areas are where a generated feature is most likely to hand something back.

Klavity gives you the automated layers on top: Sims runs AI personas across your app to find the paths nobody scripted, AutoSim turns the journeys that matter into self-healing E2E tests, and Snap captures anything that still gets through with console logs, network activity, and a reproduction trail attached. Try Klavity free and scan your next AI-built feature before your client does.