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AI UX Audit Tool

aiuxdesign.guide — Upload a screenshot of your AI product. Get a graded report on what's working, what's missing, and what to ship next — scored against 36 research-backed AI UX patterns.

What the audit does

Drop a screenshot (chat thread, agent surface, AI-powered feature) into the audit tool. The pipeline returns:

  • A score out of the patterns that apply to your surface (typically 3–8 out of 36 — most surfaces don't meaningfully invoke all 36)
  • Top gaps, ranked by severity, each with cited visible evidence from the screenshot
  • A consolidated handoff prompt you can paste into Claude Code, Cursor, or another coding assistant — it locates the affected files in your repo and applies the patterns
  • Per-gap pattern links for designers who want to go deep on one issue

The audit is free, no signup required for the first run.

How the grading works

The audit is run by Claude (Sonnet, temperature: 0) against a structured prompt that encodes all 36 patterns. The process is intentionally evidence-first, not pattern-first — earlier versions that led with "check pattern X" produced fabricated findings on surfaces where pattern X didn't apply. Current methodology, in order:

  1. Describe each surface. Identify what specifically is shown (e.g., "Claude chat thread mid-conversation", "Settings → Usage page"). Inventory the visible interactive controls. This list anchors every later "X is missing" claim — if a control wasn't in the inventory, claiming it's absent is hallucination.
  2. Select applicable patterns (max 8). From the 36-pattern library, list only the patterns that meaningfully apply to this specific surface. A billing page does not need Confidence Visualization; a chat thread does. Most surfaces invoke 3–7 patterns, not 20. Padding destroys trust in the audit, so the prompt has a hard cap.
  3. Evaluate only the applicable patterns. For each one, decide whether the surface implements it. Every finding must include an evidence field that cites a specific visible UI element ("no thumbs-up/down on AI messages in the message list"). Findings without specific evidence get dropped — they are not invented.
  4. Rank gaps + propose actions. Each gap gets a severity (critical / high / medium / low) and a "what to ship" suggestion grounded in the pattern's install guidance.

The honest-fallback case matters as much as the happy path: if the screenshot isn't an AI product surface (e.g., a marketing page, a non-AI billing screen), the audit returns 0 applicable patterns and a useful empty-state explanation — not 8 fabricated gaps.

The 36 patterns powering the audit

The pattern library is the methodology — open source, with interactive demos and install prompts for each. Patterns are organized across 8 categories:

Category Patterns Examples
Human-AI Collaboration 10 Autonomy Spectrum, Intent Preview, Human-in-the-Loop
Trustworthy & Reliable AI 8 Trust Calibration, Explainable AI, Safe Exploration
Adaptive & Intelligent Systems 4 Adaptive Interfaces, Ambient Intelligence
Natural Interaction 4 Conversational UI, Progressive Disclosure
Safety & Harm Prevention 4 Crisis Detection, Vulnerable User Protection
Performance & Efficiency 3 Intelligent Caching, Agent Status Monitoring
Privacy & Control 2 Privacy-First Design, Selective Memory
Accessibility & Inclusion 1 Universal Access Patterns

Each pattern page includes: an opinionated judgment call ("use it when / don't when / the trap"), 4 ranked takeaways, real-world examples from shipped products, an interactive demo, and an "install with Claude Code" prompt that drops the pattern into your codebase.

Also at aiuxdesign.guide

  • Designer Guides — structured learning paths covering AI UX topics
  • Daily Newsletter — curated AI UX news; pattern-tagged so updates surface relevant changes to the library
  • Prompt Library — reusable prompts for AI-powered design workflows

Tech Stack

Layer Technology
Framework Next.js 15 (App Router)
UI React 19, TypeScript
Styling Tailwind CSS v4
Database PostgreSQL on Neon (Prisma ORM)
AI Claude API (Anthropic SDK) — Sonnet for audit, Haiku for newsletter
Email Resend (transactional) + Beehiiv (newsletter delivery)
Analytics Microsoft Clarity, Vercel Speed Insights
Deployment Vercel

Getting Started

git clone https://github.com/imsaif/aiex.git
cd aiex
npm install
cp .env.example .env.local  # configure environment variables
npx prisma generate
npm run dev

Open http://localhost:3000.

Environment Variables

See .env.example for the full list. At minimum:

  • DATABASE_URL — PostgreSQL connection string
  • ANTHROPIC_API_KEY — for audit, mentor, and newsletter generation
  • RESEND_API_KEY — for transactional email
  • BEEHIIV_API_KEY + BEEHIIV_PUBLICATION_ID — for newsletter subscriber sync

Development

npm run dev          # Dev server (Turbopack)
npm run build        # Production build
npm run lint         # ESLint
npm test             # Run tests
npm run test:coverage # Tests with coverage report

Contributing

Contributions welcome — new patterns, improvements to existing ones, audit prompt refinements, bug fixes, docs. See CONTRIBUTING.md.

License

MIT — see LICENSE.

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AI UX Design Patterns - How the world's best AI products design their experiences, documented, analyzed, and continuously updated.

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