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generative-ui

Designing interfaces where AI generates UI components dynamically.

22

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Generative UI

Generative UI is when the AI creates interface elements on the fly — forms, cards, charts, layouts — rather than responding with plain text. The interface itself becomes a model output.

What Generative UI Changes

Traditional UI is designed ahead of time. Generative UI is created in the moment:

  • Static UI: Designer creates all possible screens. Developer builds them. User navigates between them.
  • Generative UI: The AI creates interface elements in response to user needs. No screen was pre-designed. This shifts the designer's role from designing screens to designing the rules, constraints, and patterns the AI uses to generate screens.

Design Constraints for Generated UI

The AI needs guardrails for what it generates:

  • Component library: A defined set of components the AI can compose (cards, tables, forms, charts, buttons)
  • Layout rules: Grid systems, spacing, hierarchy rules the AI must follow
  • Style boundaries: Colour, typography, and visual style the AI must stay within
  • Interaction patterns: How generated elements behave (clickable, editable, dismissible)
  • Accessibility requirements: Generated UI must meet accessibility standards automatically

When to Use Generative UI

Generative UI is powerful when:

  • The output is structured data that benefits from visual presentation (tables, charts, comparisons)
  • The user's request implies an interface (e.g., "show me a dashboard of...")
  • Interactive elements would be more useful than text (e.g., forms, selectors, toggles)
  • The content is highly variable and can't be pre-designed for every case

When Not to Use It

  • When plain text is clearer and simpler
  • When the generated UI could be confusing or inconsistent
  • When accessibility cannot be guaranteed
  • When the user expects a predictable, stable interface

Design Artefacts

  • Generative UI component libraries (what the AI can use)
  • Generation rules and constraints specification
  • Quality criteria for generated interfaces
  • Fallback designs for when generation fails
Repository
Owl-Listener/ai-design-skills
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