AI UI Generation
Patterns for generating, reviewing, and integrating UI components produced by AI tools (json-render, v0.app, Google Stitch, Bolt Cloud, Cursor). json-render is the first choice for multi-surface, MCP visual output, and type-safe catalog workflows. AI-generated UI is 80% boilerplate, 20% custom — the human reviews, refactors, and owns the output. These rules ensure AI output meets design system, accessibility, and quality standards before shipping.
Tool landscape as of 2026-04:
- v0.dev → v0.app (Jan 2026 rebrand) — expanded from component scaffolding to full-stack app generation with shipping targets (Vercel, Cloudflare, Railway). v0.app MCP server (
@vercel/v0-mcp) exposes generation as an MCP tool, so agents can call it programmatically.
- Google Stitch — design-first generation via
stitch.withgoogle.com/docs/mcp. Produces multi-screen apps (up to 5 interconnected screens) with React/HTML + PNG. Strong when input is a screenshot, URL, or DESIGN.md file.
- Bolt Cloud (StackBlitz, 2026) — Bolt now runs the dev environment in the cloud (no local WebContainer). Integrates with GitHub and supports persistent databases. Best for full-stack prototypes with backend + deploy.
- v0.app and Stitch both integrate with shadcn/ui styles; pair with the
shadcn apply <style> CLI (v4) to reuse generated output.
Quick Reference
Total: 8 rules across 5 categories
Decision Table — json-render vs v0.app vs Stitch vs Bolt Cloud vs Cursor
| Scenario | Tool | Why |
|---|
| Multi-surface / MCP visual output | json-render | Single catalog renders to any surface — FIRST CHOICE |
| Type-safe component catalog | json-render | Schema-driven specs with per-platform registries |
| Streaming UI from AI agents | json-render | Structured JSON specs render progressively |
| New component from scratch | v0.app | Full scaffold with shadcn/ui, Tailwind, a11y. Pair with shadcn apply <style> for style consistency |
| Full-stack app with deploy | v0.app | Jan 2026 expansion — routes, DB, auth, Vercel/Cloudflare deploy in one generation |
| Design-driven (screenshot/URL/DESIGN.md) | Google Stitch | build_site + get_screen_code / get_screen_image, up to 5 screens |
| Multi-screen flow from a visual source | Google Stitch | Strongest when grounded in an existing design |
| Full-stack prototype with persistent backend | Bolt Cloud | Cloud dev env, GitHub integration, databases |
| Incremental change in existing codebase | Cursor | Understands project context, imports, tokens |
| Refactor existing component | Cursor | Reads surrounding code, respects conventions |
| Explore visual design variations | v0.app or Stitch | v0.app for freeform, Stitch when grounded in a reference |
| Add feature to running app | Bolt Cloud | Cloud preview, full environment, persists between sessions |
| Fix bug in existing component | Cursor | Inline edits with full project awareness |
Quick Start
Structured Prompt Example
Generate a React signup form component using:
- Framework: React 19 + TypeScript
- Styling: Tailwind CSS v4 + shadcn/ui (Luma style — rounded-4xl, shadow-md elevation)
- Style: run `npx shadcn@latest apply luma` (CLI v4, Apr 2026) after generation
- Tokens: use color.primary, color.destructive, spacing.md from our design system
- A11y: ARIA labels on all inputs, error announcements via aria-live
- States: default, loading (disabled + spinner), error (inline messages), success
- Responsive: stack on mobile (<640px), 2-col on desktop
Review Example — After AI Generation
<button className="bg-[#3b82f6] text-white px-4 py-2">Submit</button>
<Button variant="default" size="md">Submit</Button>
Rule Details
json-render Integration
json-render is the first choice for AI UI generation when output must render across multiple surfaces (web, mobile, CLI, MCP). Define a catalog of components once, generate JSON specs from AI, and render on any target surface. See ork:json-render-catalog for catalog authoring patterns.
| Rule | File | Key Pattern |
|---|
| json-render Patterns | rules/json-render-patterns.md | Catalog-first: define once, render anywhere via per-platform registries |
Prompt Engineering
Structured prompts that specify framework, tokens, a11y, and states upfront.
| Rule | File | Key Pattern |
|---|
| Prompt Patterns | rules/ai-prompt-patterns.md | Constraint-first prompts with framework, tokens, a11y |
| Iteration Patterns | rules/ai-iteration-patterns.md | Multi-pass prompts for complex interactive states |
Quality Assurance
Systematic review and CI gating for AI-generated components.
| Rule | File | Key Pattern |
|---|
| Review Checklist | rules/ai-review-checklist.md | 10-point checklist for every AI-generated component |
| CI Gate | rules/ai-ci-gate.md | Automated quality gates before merge |
Design System Integration
Ensuring AI output uses design tokens and conforms to the design system.
| Rule | File | Key Pattern |
|---|
| Token Injection | rules/ai-token-injection.md | Pass token names in prompts, reject hardcoded values |
| Refactoring Conformance | rules/ai-refactoring-conformance.md | Steps to refactor raw AI output for design system |
Tool Selection & Workflow
Choosing the right AI tool and iterating effectively.
| Rule | File | Key Pattern |
|---|
| Tool Selection | rules/ai-tool-selection.md | Match tool to use case: v0, Bolt, Cursor |
| Iteration Patterns | rules/ai-iteration-patterns.md | Iterative refinement for complex states |
Key Principles
- Own the output — AI generates a draft; the engineer reviews, refactors, and is accountable for what ships.
- Tokens over literals — Never accept hardcoded colors, spacing, or typography values. Always map to design tokens.
- Constraint-first prompts — Specify framework, tokens, shadcn style (Luma/Nova/etc.), a11y, and states upfront. Vague prompts produce vague output.
- Iterative refinement — Complex components need 2-3 prompt passes: structure first, states second, polish third.
- CI is non-negotiable — Every AI-generated component goes through the same CI pipeline as hand-written code.
- Accessibility by default — Include a11y requirements in every prompt; verify with automated checks post-generation.
Anti-Patterns (FORBIDDEN)
- Shipping raw AI output — Never merge AI-generated code without human review and design system refactoring.
- Vague prompts — "Make a nice form" produces inconsistent, non-conformant output. Always specify constraints.
- Hardcoded hex/rgb values — AI tools default to arbitrary colors. Replace with OKLCH design tokens.
- Skipping CI for "simple" components — AI-generated code has the same bug surface as hand-written code.
- Using v0.app for incremental changes — v0.app generates from scratch; use Cursor for changes within an existing codebase.
- Single-pass complex components — Multi-state components (loading, error, empty, success) need iterative prompting.
- Trusting AI a11y claims — AI tools add ARIA attributes inconsistently. Always verify with axe-core or Storybook a11y addon.
Detailed Documentation
Related Skills
ork:json-render-catalog — json-render catalog authoring, schema validation, and registry patternsork:mcp-visual-output — MCP visual output rendering with json-render specsork:multi-surface-render — Cross-platform rendering from a single component catalogork:ui-components — shadcn/ui component patterns and CVA variantsork:accessibility — WCAG compliance, ARIA patterns, screen reader supportork:animation-motion-design — Motion library animation patternsork:responsive-patterns — Responsive layout and container query patternsork:design-system-tokens — Design token architecture and theming
