Skip to main content

Design-to-Code Revolution

5 min read
FrontendUx Eng

Frontend

These tools output first drafts. You ship production. The gap between those is your job now.

Ux Eng

Design systems that codify patterns beat ad-hoc screens. AI loves consistent structure.

Design-to-Code Revolution

TL;DR

  • Figma-to-code and screenshot-to-code tools exist. They produce HTML/CSS and sometimes React. It's not magic; it's a starting point.
  • The closer your design system is to code conventions, the better the output. Chaos in Figma = chaos in output.
  • Your job shifts from "pixel pusher" to "translator and quality gate." You fix what AI misreads.

In 2025, you can drop a Figma frame or a screenshot into a tool and get code. It's impressive. It's also messy. Here's what to expect and how to use it.

What Exists Today

Figma plugins: v0, Anima, Locofy, Builder.io. They inspect your frames and generate React, Vue, or plain HTML. Some use AI; some use rule-based extraction.

Screenshot-to-code: Paste a PNG, get HTML/CSS. Cursor, v0, and others do this. Works best for static, high-contrast layouts.

Design system integration: Tools that know your tokens (colors, spacing, typography) do better. Generic tools spit generic code.

What Works Well

  • Static layouts. Hero sections, marketing cards, landing page blocks. Low interactivity, high visual.
  • Consistent design systems. If your Figma uses components, auto-layout, and named styles, output quality jumps.
  • Rapid prototyping. Need a quick UI to test an idea? Design-to-code gets you 70% there fast.
  • Handoff acceleration. Designers export; you don't start from zero. You refactor, componentize, and wire up logic.

What Breaks

  • Complex interactions. Hover states, multi-step flows, animations. AI guesses; you implement.
  • Responsive behavior. AI often outputs fixed widths or naive breakpoints. Mobile-first needs human attention.
  • Accessibility. Semantic structure, ARIA, focus—often missing or wrong.
  • Framework specifics. You want Next.js App Router patterns, server components, data fetching. AI gives you a static shell.

The New Workflow

  1. Design in Figma (or equivalent) with components and structure. The more systematic, the better.
  2. Export to code with a tool that knows your stack. Expect 50–70% fidelity.
  3. You refactor: componentize, add state, wire data, fix a11y, make it responsive.
  4. You own the integration. Design-to-code gives you a scaffold. You make it live in your app.

Designer hands off a Figma file. You open it, inspect every element, manually recreate the layout in JSX/CSS. Debate spacing values. Pixel-push for hours. Ship after 2-3 days.

Click "AI-Assisted Workflow" to see the difference →

// AI Output (v0) — looks right, but...
<div style={{display: 'flex', gap: '16px', padding: '24px'}}>
<img src="/avatar.png" width={48} height={48} />
<div>
  <p style={{fontWeight: 'bold'}}>John Doe</p>
  <p style={{color: '#666'}}>Software Engineer</p>
</div>
</div>

// Your Production Version — accessible, responsive, systematic
<Card className="flex items-center gap-4 p-6">
<Avatar src="/avatar.png" alt="John Doe" size="md" />
<div>
  <Text as="h3" variant="subtitle">John Doe</Text>
  <Text variant="body-sm" color="muted">Software Engineer</Text>
</div>
</Card>

// The gap between AI output and production is your job.

Do This Next

  1. Run one design-to-code experiment. Pick a recent Figma screen (or a screenshot). Feed it to v0, Locofy, or Cursor. Measure: How much did you change before it was shippable? That's your "AI design handoff" baseline.
  2. Document your design-to-code rules for your team. Which components to trust, which to always rewrite. Turn your learnings into a short guide.