Guide: Turn AI Mockups Into a Consistent, Accessible Component Library

Design tokens are named decisions that control visual properties across an interface. Instead of manually choosing a color in every screen, you define a role such as primary action, surface, border, muted text, or destructive action. Components then use those roles consistently across web and mobile.

A useful starter token set includes color, typography, spacing, corner radius, elevation, icon size, control height, and motion. Keep the first version small. For example, a spacing scale of 4, 8, 12, 16, 24, and 32 pixels is easier to govern than dozens of arbitrary values.

Tokens should describe purpose rather than appearance. “Text primary” is more durable than “dark gray,” because the same role can receive a different value in dark mode or a high-contrast theme. “Action primary” is clearer than “brand blue,” because it tells the team where that color belongs.

Create semantic states for every important component. A button may need default, hover, pressed, focus, disabled, loading, and destructive states. A form field may need empty, filled, invalid, valid, read-only, and disabled states. Documenting these states prevents the common mistake of designing only the happy path.

Web and mobile can share the same design language without sharing identical geometry. A 44 by 44 point touch target may be appropriate on mobile, while a dense desktop data table may use a different row height. The underlying token roles, contrast rules, content hierarchy, and interaction meaning should remain aligned.

A token inventory also gives you a practical prompt for AI generation. Rather than asking for “a modern blue button,” specify the button role, supported states, minimum target size, label behavior, and where it may be used. The advice in how to write vibe prompts for consistent, production-ready UI is useful when turning these rules into repeatable generation instructions.

Accessibility should be part of the component contract, not a final visual inspection. The Web Content Accessibility Guidelines from W3C provide an established framework for perceivable, operable, understandable, and robust experiences. You do not need to memorize every criterion, but you do need a repeatable review process.

Start with structure and naming. Every input needs a visible or programmatically associated label, every icon-only control needs an accessible name, and every error should identify the affected field in plain language. A red border alone does not explain a problem to a screen reader user or to someone with color-vision deficiency.

Test the keyboard path on every web flow. A user should be able to reach controls in a sensible order, see a clear focus indicator, operate menus and dialogs, and escape an overlay without becoming trapped. For mobile, verify that touch targets are comfortably tappable and that critical actions do not depend on a gesture that some users cannot perform.

Contrast is necessary but not sufficient. The W3C technique for checking text contrast explains the minimum relationship between foreground and background for common text. Also check focus indicators, disabled states, charts, status colors, and images with text because users should not need color alone to understand a result.

Use automated checks for speed, then add human review. Browser tools and accessibility scanners can catch missing labels, invalid structure, and some contrast problems, but they will not tell you whether a checkout error is understandable or whether a focus transition makes sense. A short manual test with keyboard navigation, zoom, mobile text enlargement, and a screen reader catches issues that static tools miss.

For a broader component review, use how to turn AI-generated UI into accessible, responsive components. The key principle is simple: accessibility belongs in the reusable component, so every screen benefits from the fix.

Once the system has clear tokens, states, and data expectations, an AI application builder can do more than reproduce a screenshot. A production-oriented workflow starts with the product model, then uses generative scaffolding to establish screens, data relationships, authentication boundaries, and integration points before refining the visual layer.

In Fayz, the practical checkpoint is whether a generated component still works when it connects to a real source. A customer portal might use Supabase or PostgreSQL records, Stripe may return a payment failure, and a Slack or Zapier action may be delayed. The onboarding process should surface those conditions early instead of treating them as post-launch surprises.

The first checkpoint is the data contract. Define the fields, required values, ownership, and lifecycle of each object. A “user” may have a role, status, organization, created date, and consent record. A “payment” may have pending, succeeded, failed, and refunded states. These details determine what the component must display.

The second checkpoint is the access model. Decide who can view, create, edit, approve, export, or delete each object. Authentication is only the front door. Authorization determines what each person can do after signing in, and the interface should reflect those boundaries without treating hidden buttons as the only protection.

The third checkpoint is integration behavior. Map what happens when an API responds slowly, returns an error, sends duplicate events, or changes a field. Founders can use how to map integrations and data flows for an MVP to document these paths before connecting the UI.

The fourth checkpoint is release evidence. Run a small set of realistic scenarios: create an account, complete onboarding, submit a form, process a payment, recover from an error, and revisit the result on another device. This is where a beautiful demo becomes evidence of a usable product. For a more complete process, follow the checklist for turning a prototype into a production-ready MVP.