AI Design Output QA Checklist: What to Check Before Anything Ships

AI-generated output often fails quietly. The page loads, the component looks plausible, and the copy reads smoothly. The problems appear in the details: a missing state, a broken hierarchy, a contrast failure, a fabricated claim, or code that does not belong in the system.

Use this checklist before an AI-assisted design or build leaves your hands.

1. Brief and context

• Can you state the problem this output solves?
• Is the audience clear?
• Did the output receive the current brand tokens, content rules, and component constraints?
• Are assumptions and unknowns written down?
• Is the source material available for any factual claim?

If these answers are unclear, stop at the brief. Polishing an output with missing context creates false confidence.

2. Content accuracy

• Are product names, features, and labels correct?
• Are examples clearly marked as examples?
• Does every statistic have a source?
• Has the tool invented a customer, result, testimonial, or capability?
• Does the copy distinguish an outcome from a promise?

The existing AI design retrospective is a useful reminder that a generated page can be quietly wrong even when it looks finished.

3. Visual hierarchy

• Can the primary message be found without explanation?
• Does type size, weight, and spacing reflect the intended order?
• Are decorative elements competing with the task?
• Are cards, buttons, and sections using the established roles?
• Does the page still work when color or motion is removed?

Compare the output to the visual identity system and the color-system guidance. AI often introduces local values that look acceptable in isolation and break the wider system.

4. States and edge cases

For every interactive or data-bearing component, test:

• default;
• hover and focus;
• disabled;
• loading;
• empty;
• success;
• error;
• long and short content;
• missing or slow media.

If a state is not relevant, document why. The absence of a state should be a decision, not an accident.

5. Accessibility and responsive behavior

• Is there one meaningful H1?
• Can keyboard users reach and understand controls?
• Are focus styles visible?
• Do images have useful alt text or an intentional empty value?
• Do text and controls meet contrast and spacing expectations?
• Does the layout remain understandable at a narrow width and high zoom?

Use the W3C text-spacing guidance (opens in a new tab) when testing whether the layout survives user adjustments. A visual review is not a substitute for testing the actual page.

6. Code and system fit

• Does the output use existing components and tokens?
• Has it introduced magic numbers or duplicate patterns?
• Are names consistent with the codebase?
• Is the external interface unchanged where it should be?
• Can another person maintain the result without asking the model to explain it?

The Claude Code workflow note describes a useful boundary: AI can handle structure and repetition, while architectural judgment stays with the designer-developer.

7. Release evidence

Before publishing, save the brief, source references, review notes, and any known limitation. A release decision should answer what was checked and by whom. This is especially important when the output touches customer-facing claims, accessibility, or production code.

The checklist is not meant to slow every experiment. It separates an exploration from something that represents the brand. That distinction is the human review gate in an AI-assisted design workflow.