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Vitaly Friedman – Design Patterns For AI Interfaces (Overview)

Instructor

• Vitaly Friedman – Design Patterns For AI Interfaces
• Co-founder of Smashing Magazine
• ~20 years in UX, front-end, and interface design
• Works with orgs like European Parliament, Axel Springer

Format

• Formats:
  • Cohort-based workshop (live sessions)
  • Self-paced video course (~10 hours, ~30 lessons)
• Duration:
  • 3 days → 4–6 weeks depending on version
• Includes:
  • Recordings
  • UX checklists
  • Real-world examples library

Core Idea

Most AI products fail not because of bad AI — but bad interface design.

Instead of teaching:

• prompts
• tools
• coding

This course teaches:
How to design AI experiences that people actually use, trust, and understand

The Problem It Solves

Modern AI UX is broken:

• Chatbots are slow and frustrating
• Users must “guess prompts”
• Outputs are walls of text
• Low adoption & retention

Key insight:

Chat UI is often the wrong interface for AI.

Core Concepts & Patterns

1. Beyond Chatbots

• Move away from:
  • blank input boxes
  • endless chat threads
• Replace with:
  • structured inputs
  • visual workflows
  • guided interactions

AI should feel like a tool, not a conversation.

2. New AI Design Patterns

Some signature patterns taught:

• Style Lenses → control tone/output style
• Temperature Knobs → adjust creativity
• Prompt Presets → reduce thinking effort
• Daemons → background AI agents
• Clustering → organize outputs visually
• Task Builders → step-by-step workflows

These patterns make AI: faster, clearer, and more controllable

3. Input & Output Design

Instead of typing prompts:

• Use:
  • dropdowns
  • sliders
  • structured forms
  • visual canvases

Goal:
Reduce cognitive load and prompt friction

4. Designing for Trust

Covers:

• Why users don’t trust AI
• How to fix it with:
  • transparency
  • reasoning traces
  • confidence indicators

Trust = adoption

5. AI Workflow Design (Advanced)

• Move from linear UX → AI loops
• Support:
  • iteration
  • refinement
  • feedback cycles

Also includes:

• human-in-the-loop design
• approvals & guardrails

6. Real-World Case Studies

• 100s of examples:
  • AI assistants
  • side panels
  • infinite canvas tools
  • voice + multimodal interfaces

Focus:
What works vs fails in real products

What You Get

• 30+ video lessons
• UX frameworks & checklists
• Pattern library (very valuable)
• Real interface teardown examples

Who It’s For

Best fit:

• Product designers (UI/UX)
• AI product managers
• SaaS founders building AI features
• Frontend engineers working on AI UX

Less useful for:

• pure backend engineers
• people looking for coding tutorials

Pros

• Extremely practical UX patterns (rare in AI courses)
• Focus on real usability problems
• Helps build AI products people actually adopt
• Strong for agentic / workflow-based AI UX

Cons

• No coding or technical deep dive
• Can feel abstract without a product to apply it to
• Expensive for non-designers

Bottom Line

This course is: “How to design the interface layer of AI products”

While most AI courses teach:

• how to build models
• how to prompt

This teaches:

• how AI should feel to users

Quick Comparison Insight

• Rajiv Shah (Problem Framing) → What to build
• Vitaly Friedman (This course) → How users interact with it

Together:

• Shah = strategy
• Friedman = UX execution

 

 

See More: Rajiv Shah – AI Problem Framing for Agentic AI

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