AI Product UX Pattern Collection

AI Product UX Patterns Collection – rezza.io 🎉 Usage.so beta is coming soon! Subscribe to get early access. This is an open-source collection of popular UX patterns for AI products. The pattern library helps builders create intuitive and effective user experiences for AI-driven applications, regardless of technology or framework.

Overview

The AI Product UX Patterns Collection provides a curated set of UX patterns focused on guiding users through AI-driven interactions. It structures the AI workflow into four essential steps and introduces supportive blocks to scale and sustain products:

• Getting Context: Set the stage for understanding user needs and intent.
• Intermediary Processing: Keep users engaged as the AI analyzes and processes data.
• Presenting Results: Focus on how best to present AI-generated insights.
• Version Control: Offer users the ability to tweak and perfect outcomes across interactions.

Supportive blocks include:

• Monetization: Align product capabilities with revenue models.
• Usage Control: Manage deployment and maintain optimal user experience.

Main Loop of AI Interaction

The collection mirrors the natural progression of AI interactions: Getting Context → Intermediary Processing → Presenting Results. This framework also emphasizes Usage Control and Monetization to ensure products are user-centric, viable, and sustainable.

Supportive Blocks for Scaling AI Products

• Version Control: Manage interaction history (conversations, prompts, generative tasks). Review, revert, and select preferred outcomes from past iterations.
• Monetization: Integrate revenue-generating strategies into AI capabilities.
• Usage Control: Oversee deployment, manage load, and maintain performance.

How to Use the Patterns

1. Identify the AI interaction stage you are targeting (Getting Context, Intermediary Processing, Presenting Results).
2. Implement the corresponding patterns to optimize user experience and engagement.
3. Apply Version Control to enable iterative improvements and revert when necessary.
4. Consider Monetization and Usage Control early to ensure sustainability and reliability.

Structure

• Getting Context
• Intermediary Processing
• Presenting Results
• Version Control
• Monetization
• Usage Control
• Supportive building blocks to scale and grow your AI products

References & Acknowledgments

The collection aggregates insights from diverse AI applications and design publications to illuminate best practices in AI product UX. Acknowledgments go to the broader AI design community for contributing patterns, ideas, and methodologies that inform this library.

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Feature Overview

• Open-source collection of UX patterns for AI products
• Four-step AI interaction loop: Getting Context, Intermediary Processing, Presenting Results, Version Control
• Supportive blocks: Monetization, Usage Control
• Scalable patterns for building and maintaining AI-driven applications
• Accessible to builders regardless of underlying technology or framework
• Regular updates and community contributions via open-source model