AI Interaction Patterns

AI-specific UX patterns for designing interfaces where users interact with AI models. Covers the full interaction lifecycle: from first prompt to output verification, memory persistence, and trust building.

Source: Based on Shape of AI pattern library (CC-BY-NC-SA) by Emily Campbell.

Overview

This skill provides:

• 60+ AI interaction patterns across 6 categories
• Pattern selection decision framework
• Trust-level assessment for AI interfaces
• Wayfinding strategies for AI onboarding
• Human-in-the-loop governance patterns
• AI identity and personality design guidance
• Memory and context persistence patterns
• Anti-patterns to avoid in AI UX

How This Differs from Other UI/UX Skills

Skill	Focus
23-uiux-design-principles	Layout, hierarchy, responsive design (framework-agnostic)
21-enterprise-ai-ux	Enterprise context: challenge taxonomy, professional palettes, RBAC
22-conversation-ux	Thread management, branching data model, context switching
20-interactive-widgets	Widget protocols, rendering pipeline, state management
25-ai-interaction-patterns (this)	AI-SPECIFIC interaction logic: how users prompt, control, trust, and relate to AI

Reference Documentation

Complete Pattern Catalog

• ai-interaction-patterns - Full reference for all 60+ patterns
  • Wayfinders (8 patterns)
  • Prompt Actions (14 patterns)
  • Tuners (10 patterns)
  • Governors (13 patterns)
  • Trust Builders (7 patterns)
  • Identifiers (5 patterns)

Quick Pattern Selection

By User Problem

User Says/Feels	Apply Pattern
"I don't know what to ask"	Gallery, Suggestions, Templates
"AI didn't understand me"	Follow-ups, Nudges, Prompt Enhancer
"I want alternatives"	Variations, Branches, Randomize
"Is this accurate?"	Citations, References, Caveat
"This is taking too long"	Draft Mode, Controls, Cost Estimates
"I need AI to do something complex"	Action Plan, Stream of Thought
"Is this AI or human?"	Disclosure, Avatar, Name
"Don't store my data"	Incognito Mode, Consent, Data Ownership
"AI forgot what I said"	Memory (scoped/global/ephemeral)

By AI Product Type

Product Type	Essential Patterns	Nice-to-Have
Chat assistant	Open Input, Suggestions, Follow-ups, Memory, Disclosure	Gallery, Voice & Tone, Branches
Code copilot	Inline Action, Stream of Thought, Controls, Citations	Action Plan, Draft Mode
Image generator	Gallery, Parameters, Variations, Inpainting, Preset Styles	Draft Mode, Randomize
Document AI	Attachments, Citations, Caveat, Disclosure, Summary	Transform, Expand, Follow-ups
AI agent (agentic)	Action Plan, Controls, Verification, Stream of Thought, Cost Estimates	Memory, Consent
Voice assistant	Voice Avatar, Personality, Controls, Disclosure	Memory, Consent
Enterprise analytics	Citations, Connectors, Filters, Modes, Disclosure	Action Plan, Memory

Trust Level Decision

Is this a high-stakes domain (healthcare, finance, legal)?
  YES → CRITICAL trust: Citations + Verification + Disclosure + Caveat + Audit
  NO  →
    Is AI output mixed with human content?
      YES → HIGH trust: Disclosure + Citations + Caveat
      NO  →
        Could AI output cause harm if wrong?
          YES → MEDIUM trust: Caveat + Citations (optional)
          NO  → LOW trust: Minimal caveat, focus on UX quality

The Six Pattern Categories

1. Wayfinders

Help users construct their first prompt and get started.

Pattern	Purpose	When to Use
Gallery	Showcase what's possible	Onboarding, inspiration, capability discovery
Suggestions	Context-aware prompt starters	Cold start, idle moments, mode changes
Templates	Structured prompt scaffolds	Recurring tasks, complex prompts
Follow-ups	Conversation continuations	After every AI response
Initial CTA	First-interaction entry point	Landing pages, empty states
Nudges	Proactive guidance	When user prompt could be improved
Prompt Details	Expose generation parameters	Educational, reverse-engineering
Randomize	Serendipity exploration	Creative tools, discovery

2. Prompt Actions

Different actions users can direct AI to complete.

Pattern	Purpose	When to Use
Open Input	Natural language dialogue	Universal starting point
Inline Action	Edit within existing content	Document editors, code tools
Chained Action	Multi-step sequential tasks	Complex workflows
Regenerate	Re-run with modifications	When output needs improvement
Transform	Change content format/structure	Data processing, content adaptation
Restyle	Change visual/tonal style	Creative tools, voice adjustment
Expand	Elaborate on content	Drafting, content generation
Summary	Condense content	Reading, research, analysis
Synthesis	Combine multiple sources	Research, report generation
Describe	Generate from visual/audio input	Multi-modal tools
Auto-fill	AI-populated form fields	Data entry, profile completion
Restructure	Reorganize content structure	Documents, presentations
Madlibs	Guided prompt construction	Onboarding, structured tasks
Inpainting	Region-based selective editing	Image/video editing

3. Tuners

Adjust contextual data and settings to refine the prompt.

Pattern	Purpose	When to Use
Attachments	Upload files as context	Document analysis, RAG
Connectors	Link external data sources	Enterprise integration
Parameters	Fine-tune generation settings	Advanced users, precision tasks
Model Management	Select/switch AI models	Multi-model products
Modes	Switch AI behavior profiles	Multi-purpose tools
Filters	Narrow input/output scope	Search, data analysis
Prompt Enhancer	Auto-improve user prompts	Novice users, quality improvement
Preset Styles	Pre-configured parameter sets	Quick style selection
Saved Styles	User-created parameter presets	Returning users, consistency
Voice and Tone	Configure AI personality	Customization, brand alignment

4. Governors

Human-in-the-loop features for oversight and agency.

Pattern	Purpose	When to Use
Action Plan	Preview steps before execution	Complex/expensive tasks
Stream of Thought	Show AI reasoning in real-time	Transparency, debugging
Controls	Stop, pause, resume, queue	During generation
Draft Mode	Low-fidelity preview first	Expensive generation
Branches	Divergent exploration paths	Creative exploration
Variations	Multiple outputs for comparison	Selection, quality
Citations	Source attribution	Factual claims, research
References	Link to supporting materials	Evidence, verification
Verification	Confirmation before irreversible actions	Destructive operations
Memory	Cross-session context persistence	Personalization
Cost Estimates	Resource usage transparency	Paid/metered services
Sample Response	Quick preview before full gen	Expensive operations
Shared Vision	Align user-AI understanding	Complex instructions

5. Trust Builders

Build confidence in AI ethics, accuracy, and trustworthiness.

Pattern	Purpose	When to Use
Disclosure	Label AI content as AI-generated	Blended content, agents
Caveat	Warn about AI limitations	All AI outputs
Consent	Obtain permission for data use	Recording, training, sharing
Data Ownership	Clarify data storage/usage	Enterprise, privacy-sensitive
Watermark	Mark AI-generated media	Images, audio, video
Footprints	Attribute aggregated sources	Synthesized content
Incognito Mode	Non-persistent sessions	Sensitive topics, privacy

6. Identifiers

Distinct qualities of AI that can be modified at brand/model level.

Pattern	Purpose	When to Use
Avatar	Visual AI representation	Chat, voice, multi-agent
Personality	Tone, warmth, authority	All AI interactions
Name	AI naming strategy	Branding, trust-setting
Color	Brand and state signaling	UI theming, state changes
Iconography	AI-specific visual language	Throughout UI

CRITICAL Gotchas

Rule	Why
NEVER use photorealistic avatars unless the AI truly matches that capability	Sets unrealistic expectations, erodes trust when capabilities fall short
ALWAYS show Stream of Thought for tasks > 5 seconds	Users abandon or re-submit when they can't see progress
NEVER let Memory be a black box	Users must see, edit, and delete what AI remembers
ALWAYS offer Controls (at minimum: stop) during generation	Users need escape hatches for wrong-direction outputs
NEVER rely solely on Caveats for safety	Caveat blindness is real; design the system to be safe, then add caveats
ALWAYS distinguish AI content from human content in blended UIs	Users may unknowingly present AI work as their own
NEVER overwrite user work without Verification	Accidental overwrites destroy trust instantly
ALWAYS pair Suggestions with the ability to edit before sending	Users need to refine, not just accept blindly

When to Use This Skill

Use this skill when:

• Designing any AI-powered user interface
• Choosing interaction patterns for AI products
• Evaluating AI UX for trust and transparency
• Building prompt input experiences
• Implementing human-in-the-loop workflows
• Designing AI identity (avatar, name, personality)
• Planning AI memory and context management UX
• Auditing AI interfaces for anti-patterns

Related Skills

• 21-enterprise-ai-ux - Enterprise-specific AI design (RBAC, compliance, professional palettes)
• 22-conversation-ux - Thread management and conversation data models
• 23-uiux-design-principles - General design principles (layout, hierarchy)
• 20-interactive-widgets - Widget rendering and state management

Support

For AI interaction design questions, invoke:

• ai-ux-designer

  - AI-specific interaction pattern selection and design

• uiux-designer

  - General layout, hierarchy, and visual design

• kaizen-specialist

  - AI agent capabilities informing UX decisions

• frontend-developer

  - Implementation of AI interaction patterns