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AI UX Patterns

Standardized approaches that address common user experience challenges in AI-driven applications.

AI UX patterns help ensure that users feel informed, in control, and comfortable with the technology.

Wayfinders

Builds trust by helping users understand how the AI arrived at its conclusions.

Follow up

Get more information from the user when the initial prompt isn't sufficiently clear.

Nudges

Guide users toward actionable steps to engage with AI features, particularly helpful for those who are new to the system.

Suggestions

Proactive recommendations to spark user creativity and input.

Templates

Predefined structures for guiding AI interactions effectively.

Inputs

Submit the user's prompt to the AI within its surrounding context.

Auto-Fill

Saving time by pre-populating fields with relevant suggestions.

Inline action

Direct, contextual AI suggestions for real-time workflows.

Madlibs

Structured templates to inspire and shape user inputs.

Open input

Freeform prompts to let users guide AI interactions.

Remix / Blend

Combining AI outputs to produce creative variations.

Summary

Condensed, high-level overviews of complex AI-generated content.

Synthesis

Merging inputs or outputs to create cohesive results.

Token layering

Visualizing how tokens influence AI’s understanding and response.

Tuners

Let users refine or remix their prompt to get improved results.

Filters

Refining AI outputs by applying targeted criteria or styles.

Inpainting

Editing parts of AI-generated images for precise adjustments.

Model Management

Tools to select, compare, and switch between AI models.

Parameters

Adjustable settings for fine-tuning AI performance.

Personal voice

Tailoring AI interactions to reflect individual preferences.

Primary sources

Connecting users to original data behind AI-generated results.

References

Tools to link AI outputs to external or internal resources.

Workflows

Streamlined processes for managing AI tasks and outputs.

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