AI-Driven UI Design Figma Plugin
Budget: $12 – $30 SGD
1. Objective
Build a Figma plugin that leverages a Large Language Model (LLM) to generate UI designs based on natural language prompts, using components and styles from an existing Figma design system. The plugin should be built using TypeScript and HTML.
2. Key Features
2.1 LLM-Powered Design Generation
Prompt-Based UI: Text input field where users describe the design they want (e.g., “Create a mobile signup form with branding”).
LLM Integration:
Connect to a supported LLM (e.g., OpenAI, Anthropic).
Convert user input into structured UI layouts (e.g., JSON or design DSL).
Design System Integration:
Automatically use components (buttons, inputs, cards) and styles (colors, typography, spacing) from a linked Figma design system file or library.
Map generated design structure to matching design system components.
Maintain design consistency with constraints, variants, and auto-layout settings.
2.2 Design System Linking
Design System Selection:
Plugin should allow users to select a published Figma library or design system within the current workspace.
Component Matching Engine:
Based on component names, tags, or descriptions, match LLM-suggested UI elements to real design system components.
If no match is found, fall back to generic Figma components and prompt the user.
2.3 Design Rendering in Figma
Translate the AI-generated structure into real Figma nodes using the selected design system’s components.
Place components in frames with auto-layout, padding, and responsive settings.
Enable user to edit text and labels in-place post-generation.
3. Technical Requirements
3.1 Frontend
Technology: TypeScript, HTML, CSS (or Tailwind optional).
UI: Simple sidebar UI with input field, output preview, and design system selector.
3.2 Backend / Integration
LLM API: Integration with OpenAI, Anthropic, or other LLM APIs.
Figma Plugin API: Used to:
Access and render components
Query design system files
Insert and manipulate nodes
3.3 Design System Parsing
Parse component sets, styles, and variants from selected design system.
Create an internal map (e.g., a lookup dictionary) for matching generated components.
4. User Flow
User opens plugin in Figma.
User selects a design system or links an existing published library.
User types a natural language prompt, e.g., “Design a pricing section with 3 cards and a CTA button.”
LLM returns structured layout, plugin maps elements to the design system.
Design is inserted into the Figma canvas using real components.
User edits or fine-tunes the design directly.
5. UI Design (Basic)
Panel includes:
Text prompt field
Generate button
Dropdown to select linked design system
Option to refresh or re-scan components
Preview or error feedback
6. Success Metrics
% of components correctly mapped to design system items.
User satisfaction with AI-generated layouts.
Time saved in building consistent UIs.
Adoption rate across design teams using Figma libraries.
7. Future Enhancements
Component usage analytics from design system
Support for multiple libraries and fallback strategies
AI suggestions for improving accessibility or responsiveness
Build a Figma plugin that leverages a Large Language Model (LLM) to generate UI designs based on natural language prompts, using components and styles from an existing Figma design system. The plugin should be built using TypeScript and HTML.
2. Key Features
2.1 LLM-Powered Design Generation
Prompt-Based UI: Text input field where users describe the design they want (e.g., “Create a mobile signup form with branding”).
LLM Integration:
Connect to a supported LLM (e.g., OpenAI, Anthropic).
Convert user input into structured UI layouts (e.g., JSON or design DSL).
Design System Integration:
Automatically use components (buttons, inputs, cards) and styles (colors, typography, spacing) from a linked Figma design system file or library.
Map generated design structure to matching design system components.
Maintain design consistency with constraints, variants, and auto-layout settings.
2.2 Design System Linking
Design System Selection:
Plugin should allow users to select a published Figma library or design system within the current workspace.
Component Matching Engine:
Based on component names, tags, or descriptions, match LLM-suggested UI elements to real design system components.
If no match is found, fall back to generic Figma components and prompt the user.
2.3 Design Rendering in Figma
Translate the AI-generated structure into real Figma nodes using the selected design system’s components.
Place components in frames with auto-layout, padding, and responsive settings.
Enable user to edit text and labels in-place post-generation.
3. Technical Requirements
3.1 Frontend
Technology: TypeScript, HTML, CSS (or Tailwind optional).
UI: Simple sidebar UI with input field, output preview, and design system selector.
3.2 Backend / Integration
LLM API: Integration with OpenAI, Anthropic, or other LLM APIs.
Figma Plugin API: Used to:
Access and render components
Query design system files
Insert and manipulate nodes
3.3 Design System Parsing
Parse component sets, styles, and variants from selected design system.
Create an internal map (e.g., a lookup dictionary) for matching generated components.
4. User Flow
User opens plugin in Figma.
User selects a design system or links an existing published library.
User types a natural language prompt, e.g., “Design a pricing section with 3 cards and a CTA button.”
LLM returns structured layout, plugin maps elements to the design system.
Design is inserted into the Figma canvas using real components.
User edits or fine-tunes the design directly.
5. UI Design (Basic)
Panel includes:
Text prompt field
Generate button
Dropdown to select linked design system
Option to refresh or re-scan components
Preview or error feedback
6. Success Metrics
% of components correctly mapped to design system items.
User satisfaction with AI-generated layouts.
Time saved in building consistent UIs.
Adoption rate across design teams using Figma libraries.
7. Future Enhancements
Component usage analytics from design system
Support for multiple libraries and fallback strategies
AI suggestions for improving accessibility or responsiveness