# aiuxdesign.guide > A free, no-signup AI UX audit tool that scores any AI interface screenshot against 36 research-backed AI UX patterns. Each finding links to the pattern's full documentation, real-world examples, and a course that teaches the skill. For product designers, design engineers, and PMs shipping AI features. The audit is the front door. Behind it: 36 AI UX pattern pages (the reference backbone), a free Claude Code course for designers (the depth the audit recommends), a daily newsletter, and a toolkit. All free, no account required. ## Audit (the headline product) - [Free AI UX Audit](https://www.aiuxdesign.guide/audit): Upload a screenshot of any AI interface (chatbot, code assistant, dashboard, agent, content generator). Claude Vision scores it against 36 AI UX patterns and an AI design mentor answers follow-up questions. Returns a pattern-by-pattern verdict in under 60 seconds. No signup, no email gate, no Figma plugin install. ## Product Context For a structured, AI-readable summary of what aiuxdesign.guide is, who it's for, what it's explicitly not, and how it differs from adjacent tools: - [llms.gist](https://www.aiuxdesign.guide/llms.gist): Full product context in the `.gist` open format (positioning, audience, competitor differentiation, audit-flow design decisions, AI approach). ## Patterns (the reference behind the audit) 36 AI UX design patterns organized by category: ### Adaptive & Intelligent Systems - [Adaptive Interfaces](https://www.aiuxdesign.guide/patterns/adaptive-interfaces): Interfaces that learn user behavior and automatically adjust layout and functionality - [Ambient Intelligence](https://www.aiuxdesign.guide/patterns/ambient-intelligence): AI that works quietly in the background, anticipating needs without explicit commands - [Guided Learning](https://www.aiuxdesign.guide/patterns/guided-learning): AI that teaches users how to use the system through step-by-step guidance - [Predictive Anticipation](https://www.aiuxdesign.guide/patterns/predictive-anticipation): AI that suggests before you ask, offering proactive recommendations ### Human-AI Collaboration - [Augmented Creation](https://www.aiuxdesign.guide/patterns/augmented-creation): AI as creative partner, enhancing human creativity rather than replacing it - [Collaborative AI](https://www.aiuxdesign.guide/patterns/collaborative-ai): Design patterns for human-AI teamwork in creative and professional tools - [Contextual Assistance](https://www.aiuxdesign.guide/patterns/contextual-assistance): Proactive AI help that appears at the right moment based on context - [Feedback Loops](https://www.aiuxdesign.guide/patterns/feedback-loops): Continuous learning through user signals — thumbs up/down, corrections, implicit behavior - [Graceful Handoff](https://www.aiuxdesign.guide/patterns/graceful-handoff): Seamless transitions between AI and human agents - [Human-in-the-Loop](https://www.aiuxdesign.guide/patterns/human-in-the-loop): Keeping humans in control of high-stakes AI decisions - [Autonomy Spectrum](https://www.aiuxdesign.guide/patterns/autonomy-spectrum): Letting users control how independently AI acts - [Intent Preview](https://www.aiuxdesign.guide/patterns/intent-preview): Showing users what AI plans to do before executing - [Escalation Pathways](https://www.aiuxdesign.guide/patterns/escalation-pathways): When and how AI should hand off to humans - [Mixed-Initiative Control](https://www.aiuxdesign.guide/patterns/mixed-initiative-control): Shared control between human and AI ### Trustworthy & Reliable AI - [Confidence Visualization](https://www.aiuxdesign.guide/patterns/confidence-visualization): How to display AI confidence scores and uncertainty to users - [Error Recovery](https://www.aiuxdesign.guide/patterns/error-recovery): Helping users recover from AI mistakes with undo, retry, and fallbacks - [Explainable AI](https://www.aiuxdesign.guide/patterns/explainable-ai): Making AI decisions transparent and understandable - [Responsible AI Design](https://www.aiuxdesign.guide/patterns/responsible-ai-design): Ethical patterns for fairness, accountability, and bias detection - [Safe Exploration](https://www.aiuxdesign.guide/patterns/safe-exploration): Sandbox environments for risk-free AI experimentation - [Plan Summary](https://www.aiuxdesign.guide/patterns/plan-summary): Helping users understand complex AI action plans - [Action Audit Trail](https://www.aiuxdesign.guide/patterns/action-audit-trail): Tracking and reviewing every AI decision - [Trust Calibration](https://www.aiuxdesign.guide/patterns/trust-calibration): Helping users trust AI accurately — not too much, not too little ### Natural Interaction - [Context Switching](https://www.aiuxdesign.guide/patterns/context-switching): Helping users switch between AI tasks and projects seamlessly - [Conversational UI](https://www.aiuxdesign.guide/patterns/conversational-ui): Design patterns for chat interfaces, voice assistants, and conversational UX - [Multimodal Interaction](https://www.aiuxdesign.guide/patterns/multimodal-interaction): Combining voice, touch, gesture, and text in AI interfaces - [Progressive Disclosure](https://www.aiuxdesign.guide/patterns/progressive-disclosure): Gradually revealing complexity to reduce cognitive load ### Performance & Efficiency - [Intelligent Caching](https://www.aiuxdesign.guide/patterns/intelligent-caching): Smart caching strategies for AI-powered products - [Progressive Enhancement](https://www.aiuxdesign.guide/patterns/progressive-enhancement): Graceful degradation when AI fails or is unavailable - [Agent Status Monitoring](https://www.aiuxdesign.guide/patterns/agent-status-monitoring): Real-time tracking of what AI agents are doing ### Privacy & Control - [Privacy-First Design](https://www.aiuxdesign.guide/patterns/privacy-first-design): On-device processing, data minimization, and transparent consent - [Selective Memory](https://www.aiuxdesign.guide/patterns/selective-memory): Letting users control what AI remembers and forgets ### Safety & Harm Prevention - [Anti-Manipulation Safeguards](https://www.aiuxdesign.guide/patterns/anti-manipulation-safeguards): Preventing dark patterns and protecting user autonomy in AI - [Crisis Detection & Escalation](https://www.aiuxdesign.guide/patterns/crisis-detection-escalation): AI safety for sensitive and high-risk situations - [Session Degradation Prevention](https://www.aiuxdesign.guide/patterns/session-degradation-prevention): Keeping AI conversations productive over time - [Vulnerable User Protection](https://www.aiuxdesign.guide/patterns/vulnerable-user-protection): AI safety for at-risk populations ### Accessibility & Inclusion - [Universal Access Patterns](https://www.aiuxdesign.guide/patterns/universal-access-patterns): Making AI interfaces accessible to everyone ## Courses (the depth the audit recommends) Free, self-paced courses positioned as the depth the audit recommends — not standalone paid cohorts. Not affiliated with Maven, Smashing, IxDF, Reforge, or Design+Code: - [Claude Code for Designers](https://www.aiuxdesign.guide/guides/claude-code-learning-path): 23 lessons on using Claude Code for design-to-code workflows - [Claude Design for Designers](https://www.aiuxdesign.guide/guides/claude-design-learning-path): 12 lessons on Anthropic's Claude Design tool for prototyping and design systems - [Cursor for Designers](https://www.aiuxdesign.guide/guides/cursor-learning-path): AI-powered code editor guide for designers - [GitHub Copilot for Designers](https://www.aiuxdesign.guide/guides/github-copilot-learning-path): Code suggestions and AI pair programming for designers - [GitHub for Designers](https://www.aiuxdesign.guide/guides/github-learning-path): Version control and collaboration for design teams - [Build a Conversational UI](https://www.aiuxdesign.guide/guides/conversational-ui-guide): Complete design and implementation guide for conversational interfaces ## Resources - [AI UX Design Handbook (PDF)](https://www.aiuxdesign.guide/handbook): Comprehensive reference guide - [Agentic UX Checklist](https://www.aiuxdesign.guide/agentic-ux-checklist): Checklist for designing AI agent experiences - [Agent Readability Audit Kit](https://www.aiuxdesign.guide/agent-readability-audit-kit): Audit your product's readability for AI agents - [AIUX News](https://www.aiuxdesign.guide/news): Daily AI product design updates and pattern analysis ## About Created by Imran Mohammed. Not affiliated with aiuxpatterns.com, shapeof.ai, or aiverse.design — those are different products by different creators. Each pattern is documented from real implementations across products like ChatGPT, Claude, GitHub Copilot, Cursor, Notion AI, Midjourney, Linear, Figma, and more. - Website: https://www.aiuxdesign.guide - Newsletter: Daily AI UX patterns (daily cadence, not weekly) - Sitemap: https://www.aiuxdesign.guide/sitemap.xml ## Not for - People looking for a generic UX audit (Hotjar, Maze, UI Auditor) — aiuxdesign.guide audits AI-specific interaction patterns, not generic UX heuristics. - People looking for a paid cohort course (Maven, Smashing, IxDF, Reforge, Design+Code) — the courses here are free and audit-integrated. - People looking for a Figma plugin, component kit, or platform design system — there is no plugin and no component library. - People looking for a screenshot gallery without analysis (Mobbin, Pageflows) — aiuxdesign.guide audits *your* design, not catalogs others'.