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AI-Generated Prototypes

5 min read
Ux Eng

Ux Eng

AI gets you 80% there. You own the last 20% — accessibility, coherence, edge cases.


AI-Generated Prototypes

TL;DR

  • AI can turn sketches, wireframes, or text descriptions into clickable prototypes. Fast. Not perfect.
  • Use AI for rapid iteration and exploration. You verify layout, interaction, and accessibility.
  • The bridge between design and code is your job. AI is a power tool, not a replacement.

Design-to-code tools have been around for years. AI makes them better — and more dangerous. You can go from "a rough sketch" to "something that looks like an app" in minutes. But "looks like" isn't "works like." Your job as a UX engineer is to close that gap.

What AI Does Well

Layout and structure:

  • Given a sketch or description, AI can generate a reasonable component structure. Flexbox, grid, basic responsiveness.
  • Good for first pass. You refine.

Styling:

  • Colors, typography, spacing — AI can follow a design system if you give it tokens. It will also invent things. Verify.
  • Use design tokens. Constrain the output.

Boilerplate interactions:

  • Buttons, forms, basic navigation. AI knows common patterns. It'll produce something usable.
  • Edge cases (loading states, errors, empty states) — AI often forgets. You add them.

What AI Misses

Accessibility:

  • ARIA labels, focus order, keyboard nav, contrast — AI will sometimes get these. Often it won't.
  • Always run an a11y check. Add what's missing. This is non-negotiable.

Design system coherence:

  • AI might mix styles, use wrong components, or ignore your spacing scale. One-off prototypes are fine; production-ready needs your eye.
  • Treat AI output as a draft. Apply design system rules in your edit pass.

Interaction details:

  • Hover states, transitions, micro-interactions — AI can add some. It won't match your design system's motion language.
  • You define and enforce consistency.

Edge cases and states:

  • Empty, loading, error, success. AI tends toward the happy path. Your prototypes need the full set.
  • Add them. Document them. Make them part of the handoff.

The Workflow

  1. Generate — Sketch or describe. Get AI output. Don't expect perfection.
  2. Audit — Layout, spacing, components. Does it match the design system?
  3. Accessibility pass — Semantics, keyboard, screen reader. Fix what's broken.
  4. Add states — Loading, error, empty. AI won't. You do.
  5. Handoff — Clean, documented, ready for dev. Your name on it. Your standards.

When to Use AI, When to Build

Use AI for:

  • Early exploration, "what if we tried X?"
  • Speeding up first drafts for stakeholder review
  • Learning a new pattern or component structure

Build by hand when:

  • It's going to production
  • Accessibility is critical (and it usually is)
  • Design system compliance matters
  • The design is complex or novel

AI Disruption Risk for UX Engineers

Moderate Risk

SafeCritical

AI automates routine work. Strategy, judgment, and human touch remain essential. Moderate risk for those who own the outcomes.

Manual process. Repetitive tasks. Limited scale.

Click "With AI" to see the difference →

Quick Check

What remains human when AI automates more of this role?

Do This Next

  1. Run one experiment — Sketch a simple screen. Generate with AI. List everything you had to fix. That's your QA checklist.
  2. Create an accessibility checklist — Run every AI-generated prototype through it. Make it a habit.
  3. Define "production-ready" — What must be true before you hand off? Document it. Apply it to AI output before it reaches dev.