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ai-interaction-patterns

Catalog 60+ AI interaction patterns across six categories for AI UX design.

Updated Mar 23, 2026
Search Tags:#memory#trust#human-in-the-loop#prompt-design#ux-patterns#ai-ux#ai-interaction
One-click install
npx skills add https://github.com/esperie/midas --skill ai-interaction-patterns-esperie
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-interaction-patterns
Source: https://github.com/esperie/midas/tree/main/.claude/skills/25-ai-interaction-patterns
Command: npx skills add https://github.com/esperie/midas --skill ai-interaction-patterns-esperie

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI-powered interfaces often suffer from inconsistent interaction patterns, low trust, and fragmented onboarding. This Skill codifies 60+ AI interaction patterns across six categories to standardize UX design, improve usability, and support governance across AI products.

Core Features & Use Cases

  • 60+ AI interaction patterns across six categories (Wayfinders, Prompt Actions, Tuners, Governors, Trust Builders, Identifiers) with practical guidance for onboarding, prompting, context management, and trust.
  • Pattern catalog and decision frameworks to help product teams select appropriate patterns for chat assistants, copilots, and enterprise AI tools.
  • Design guidance on memory, citations, disclosure, verification, data ownership, consent, and incognito modes, plus governance patterns for human-in-the-loop workflows.
  • Clear references and source material for Shape of AI patterns, with implementation notes for Kailash SDK integration.

Quick Start

Draft an onboarding flow for a chat AI using Wayfinders (Gallery, Suggestions, Templates) and Follow-ups.

Frequently Asked Questions about ai-interaction-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What are AI interaction patterns and why do I need them for UX design?▼

AI interaction patterns are standardized UX solutions for chat assistants and copilots. You need them to resolve inconsistent interfaces, low user trust, and fragmented onboarding by applying proven structures for prompting, memory, and governance.

How do I design an onboarding flow for an AI chat assistant?▼

Design AI onboarding flows by applying Wayfinder patterns like Galleries, Suggestions, and Templates, alongside Follow-ups. This guides users effectively, helping them understand prompt actions and context management within enterprise AI tools.

How should I handle memory and citations in AI copilot interfaces?▼

Handle AI copilot memory and citations using Trust Builder and Governor patterns. These provide design guidance for context management, source disclosures, verification, and data ownership to support human-in-the-loop workflows.

Does this AI UX pattern catalog cover human-in-the-loop governance?▼

Yes, the AI UX pattern catalog covers human-in-the-loop governance. It includes Governor patterns that offer implementation guardrails for verification, disclosures, consent, and incognito modes to ensure safe AI workflows.

What is the best way to standardize prompt UX across enterprise AI tools?▼

The best way to standardize prompt UX is applying Prompt Actions and Tuner interaction patterns. These decision frameworks help product teams select appropriate UX designs for consistent prompting across enterprise AI tools.

When should I not use standardized patterns for AI interface design?▼

Standardized AI interaction patterns may be limiting for highly novel interface paradigms requiring entirely bespoke navigation. If your AI tool operates outside standard chat or copilot contexts, these six categories might not fully address unique structural constraints.

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