Niharika P. Pujari notes that AI can now produce a surprising amount of frontend code from short prompts: forms, tables, modals, settings pages, or dashboards that often look usable immediately. While this accelerates draft creation, it also creates a risk: the first UI version can appear more complete than it actually is.
Build and render are only the starting line
Teams typically run the easiest checks first: does the code compile, does the page render, are there console errors, does the component appear in the browser. Those checks matter, but they are only the starting line. A page can render while a form is difficult to complete; a modal can appear while focus remains behind it; a generated test can pass while the user flow is broken.
AI output often has a polished surface — well‑formatted code, plausible component names, and a test file — which can reduce reviewers' inclination to slow down and verify that the interface actually works. A useful evaluation approach treats generated frontend code as a draft until user behavior has been validated.
Start with the structure of the page
Before diving into complex behaviors, check whether the generated UI uses sound structure and semantics. Frontend evaluation should include native HTML usage, not just visual layout. A button should generally be a button, not a clickable div; navigation should use links, not elements that merely look like links; form fields should have labels correctly connected.
These details are easy to miss because the UI may look fine without them, but they affect navigation, how assistive technologies interpret the page, and future maintainability. AI tools sometimes pick generic containers where native elements would be better, or they add ARIA attributes incorrectly. ARIA (Accessible Rich Internet Applications), defined by the W3C, is useful when native HTML is insufficient, but it should not substitute for the right HTML element. The first evaluation question should be: did the generated code use the right building blocks?
Check the keyboard path
A practical frontend evaluation must include the keyboard path through the interface. Many users rely on keyboards or keyboard‑like navigation, and keyboard testing reveals interaction model problems.
The simplest test is often the most revealing: put the mouse aside and try to complete the task. If the flow becomes confusing, the generated code is not ready. Can you reach essential controls? Is the focus order logical? Can you open and close a dialog without a mouse? When the dialog closes, does focus return to a sensible place?
These checks are crucial for AI‑generated UI because AI can produce interactions that work for the obvious mouse path but fail for keyboard users. For example, a custom dropdown may open on click and look finished, but not respond correctly to keyboard input. That is not an edge case — it directly affects usability.
Test focus, not just clicks
Click‑based tests are useful but can mask important problems. A test that clicks a button and waits for a success message can pass even when the same flow is frustrating for someone navigating by keyboard.
Focus behavior deserves its own attention, especially when the UI changes after user actions. On form submission failure, the user should not be left guessing: the error should be visible, connected to the relevant field when appropriate, and reachable so the user can recover. Often focus should move to the first error or to a summary that explains what needs attention.
The same applies to modals: when a modal opens, focus should move into it; when it closes, focus should return to the element that opened it. These small implementation details greatly affect whether the UI feels predictable.
Evaluate what happens when things go wrong
Generated frontend code usually looks best on the happy path: the user fills everything in correctly, the network responds quickly, the data shape is exactly as expected. Real interfaces spend a lot of time off that path.
Practical evaluation should check missing, delayed, empty, invalid, or unexpected data states — in other words, loading, error, and empty states. Loading states should communicate that something is in progress; error states should explain the problem and the next steps; empty states should make clear whether there's nothing to show, whether the user needs to act, or whether something failed quietly.
These cases are often deferred because the happy path makes the screen look finished. But users will encounter imperfect paths. A component may include a spinner because the prompt requested one, but that does not imply the loading experience is useful. An error message like "Something went wrong" without recovery options is inadequate. Evaluation must include these scenarios because they are where many real user experiences break.
Test the full user flow
Component‑level checks are helpful, but they don't always reveal problems that appear when components are combined into larger flows.
Evaluate AI‑generated frontend code through user tasks: can someone start the flow, understand what's expected, recover from mistakes, submit successfully, and see the resulting state? Does the interface still work on smaller screens? Does state remain consistent if the user goes back, edits, or retries after a failure?
Playwright‑style end‑to‑end tests or similar tooling can help. The goal isn't to automate every interaction but to protect the flows that matter most: a good test determines whether the user can complete the task the component is intended to support.
Use accessibility checks, but do not stop there
Automated accessibility checks are valuable and should be part of the evaluation process: they catch missing labels, invalid ARIA, some contrast issues, landmark problems, and other common mistakes. They are particularly helpful when AI generates code quickly and issues could otherwise become repeated patterns.
However, automated scans are not a complete accessibility review. They cannot fully judge whether a flow is understandable, whether focus movement feels natural, or whether instructions are clear. Passing an automated scan does not mean the UI is accessible — it means some common problems were not detected. The strongest approach combines automated tools with behavior‑based review: run the tools, but also use the interface (navigate by keyboard, trigger errors, test empty states) and inspect whether native HTML could do more.
Review the generated tests too
AI often generates tests along with code. That’s promising, but generated tests need the same scrutiny as generated code.
Generated tests frequently reflect the current implementation: they assert that certain text appears, a function was called, or a component rendered. Such checks aren't useless, but they can foster false confidence if they don't test meaningful behavior. Ask what failures these tests would catch: would a broken validation fail them? Would a non‑working retry button or broken keyboard navigation cause failures? If not, the tests document the implementation rather than protect the user experience.
AI can help write better tests, but prompts must specify the behavior that matters — validation recovery, loading behavior, successful submission, focus movement — and humans must review the results.
Decide what evidence is enough
Not every UI change requires the same level of evaluation. A small copy update doesn't need the same review as a new checkout flow, onboarding, or account settings page. Teams must exercise judgment.
Match evaluation depth to the change's risk. If the generated code affects a critical flow, collects user input, changes navigation, introduces custom interactions, or handles important status messages, it deserves deeper testing. If it reuses stable components in familiar patterns, a lighter review may suffice.
You don't need a checklist for every pull request; clarity about what evidence is enough will do. For some changes, a quick review and a component test are fine. For others, expect keyboard testing, accessibility checks, error‑state reviews, and a user‑flow test. The point is to avoid treating all generated code as equally trustworthy just because it looks polished.
Human review still matters
AI can generate code and suggest tests, but it cannot fully understand the product, the users, or the trade‑offs behind frontend decisions. It doesn't know which flows matter most, which interaction patterns users rely on, or where inconsistency will confuse.
That's why human reviewers remain central. Their role is to decide whether the generated solution fits the system and supports the user's task. Sometimes that means accepting the generated code; sometimes it means requesting simpler native elements, reusing an existing component, improving error recovery, or adding tests that reflect real behavior. As AI generates more code, this judgment becomes increasingly important.
What to actually test
When AI writes frontend code, teams should test the interface parts users depend on: structure, keyboard access, focus behavior, loading and error states, form validation, responsive behavior, accessibility checks, and full user‑flow completion. Also review generated tests to ensure they protect behavior rather than merely confirm implementation.
The aim isn't to slow down AI‑assisted development but to make it safer. If AI shortens the time to a first draft, teams have an opportunity to invest more engineering attention into evaluation, user behavior, and quality. In frontend development, the value of AI is not just faster code — it's the chance to shift more effort toward making sure the software actually works.
AI‑generated UI should not be trusted because it looks complete; it should be trusted because the team has checked the right things.
Author's note: I used AI assistance lightly for phrasing, editing, and tightening parts of this draft. The ideas, structure, examples, and final review are my own.
The views expressed are my own and do not represent those of my employer.
