AI UX Patterns is an open-source collection of UX patterns for AI products, primarily intended for developers and designers building AI-driven applications, helping them address the complexities of AI UX design and create applications that are both powerful and easy to use, without being limited to any underlying technology or framework.
Purpose The collection is used to organize and present common UX patterns in AI products, giving builders a reference structure and steps when designing AI interactions, lowering the design barrier and improving consistency in user experience.
Main Capabilities
- Main Generative Loop: Breaks down AI interaction into several core stages, forming the main loop.
- Getting Context: Builds the foundation for understanding user needs and intent.
- Intermediary Processing: Keeps users engaged while AI analyzes and processes data.
- Presenting Results: Focuses on how to best display AI-generated insights.
- Supportive Blocks: Used to expand and grow AI products, including:
- Version Control: Allows users to manage interaction history, such as conversations, prompts, or generation tasks, review and roll back to previous states, and select preferred results from past iterations.
- Monetization: Helps turn AI technology into a viable business model, combining product capabilities with revenue generation.
- Usage Control: Oversees the deployment of AI solutions, ensuring reasonable use, managing load, and maintaining the best interaction experience.
How to Use The materials do not provide specific installation commands, code, or operating steps. The collection is presented as a pattern reference, and builders can refer to and apply the summarized interaction stages and supportive blocks when designing AI products.
Applicable Scenarios Suitable for user experience design of AI-driven applications, especially products that need to handle interactions such as conversations, prompts, and generation tasks. Regardless of the underlying technology or framework, these patterns can be referenced to organize interaction flows.
Limitations The materials do not provide specific information such as fees, pricing, platforms, or effectiveness; they also do not provide directly executable code or installation commands. The collection describes itself as learning and organization from existing AI applications, articles, books, and research, and the specific results of use need to be evaluated based on actual circumstances.
