ai-interaction-patterns

AI interaction patterns are repeatable UX structures that balance usability, trust, and transparency in AI interfaces. You need them when designing onboarding, memory, prompts, or governance flows to reduce ambiguity and drift in AI-powered products.

Design human-in-the-loop governance by applying Governor and Trust Builder patterns. These AI UX patterns provide structured control mechanisms for AI assistant workflows, ensuring users maintain oversight and clarity during automated customer-support or enterprise tasks.

Yes, these AI interaction patterns apply across both enterprise and consumer products. The catalog covers six categories—Wayfinders, Prompt Actions, Tuners, Governors, Trust Builders, and Identifiers—offering scalable guidance for onboarding, memory management, and prompt control.

The best way to structure AI onboarding and prompt control is using Wayfinders and Prompt Actions patterns. These AI UX patterns guide users through initial interactions and provide clear, actionable prompt controls to improve interface clarity and trust.

Manage AI memory and transparency by implementing Tuners and Identifiers patterns. These AI UX patterns help users understand what the system remembers, adjust AI behavior, and verify outputs, building sustained trust through visible system state and memory management.

AI interfaces lack trust when transparency and controllability are missing. Fix this by applying Trust Builder and Governor patterns from this AI UX catalog, which provide practical guidance for clarifying AI actions, managing memory, and enabling human oversight.