AI products require different UX patterns than traditional software. Users interact with probabilistic systems that sometimes fail, often surprise, and always need clear expectations.

Core Principles of AI UX

1. Set Clear Expectations

• Tell users what the AI can and can't do
• Show confidence levels when appropriate
• Use language like "suggested" and "draft" rather than "the answer"
• Explain limitations upfront rather than after failures

2. Design for Failure AI will fail. Design for it:

• Provide easy ways to correct or override AI suggestions
• Show alternatives: "Here are 3 options" rather than "Here's the answer"
• Allow users to fall back to manual processes
• Make feedback mechanisms prominent (thumbs up/down, edit, regenerate)

3. Progressive Disclosure Don't overwhelm users with AI capabilities:

• Start simple and reveal advanced features as users gain confidence
• Use defaults that work for most users
• Hide complexity behind "Advanced" options
• Onboard users with examples, not documentation

4. Transparency Without Overload

• Show the AI's reasoning when it adds value (not always)
• Provide source citations for factual claims
• Indicate when the AI is uncertain
• Avoid false precision (don't say "87.3% confident" when you mean "fairly confident")

Common AI UX Patterns

Suggestions and autocomplete: The AI provides options; the user selects or modifies. Examples: Gmail Smart Compose, GitHub Copilot, Notion AI

Chat and conversation: Natural language interaction for complex tasks. Examples: ChatGPT, customer support bots, AI assistants

Automated actions with confirmation: The AI prepares an action; the user approves it. Examples: AI email drafts, automated scheduling, code refactoring

Background intelligence: The AI works silently, surfacing insights only when relevant. Examples: spam filtering, anomaly detection, smart notifications

Anti-Patterns to Avoid

• Anthropomorphizing — Don't make the AI seem more human than it is
• Hiding AI involvement — Users should know when they're interacting with AI
• No escape hatch — Always let users bypass the AI
• Ignoring errors — Acknowledge and learn from AI mistakes
• Feature overload — Focus on one core AI interaction, not 20 mediocre ones