Generative Development Framework
GDF.ai
  • Intro to GDF-FSE
    • Generative AI, Large Language Models, ChatGPT?
    • Knowledge Areas
    • Access a Chat Based LLM
    • Why GDF?
    • Expectations
  • Limitations
  • Prompting
    • Prompt Patterns
    • Prompt Context
    • Prompt Stores
    • Prompt Operators
    • Prompt Chaining
  • Security
    • Protecting Data
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    • Protection Stores
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    • Social Engineering Testing with AI
  • Subject Knowledge Areas
    • Ideation
      • Identifying a Problem Statement
      • Plan and Prioritize Features
      • Develop User Stories
      • Requirement Gathering
      • Ideation Prompting
      • Ideation Template
    • Specification
      • Specifying Languages
      • Specifying Libraries
      • Specifying Project Structures
      • Specify Schemas
      • Specifying Elements
      • Specifying API Specs
    • Generation
      • Generating UI Elements
      • Generating Mock Data
      • Generating Schemas
      • Generating Parsers
      • Generating Databases
      • Generate Functions
      • Generate APIs
      • Generate Diagrams
      • Generating Documentation
    • Transformation
      • Converting Languages
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    • Replacement
      • Replacing Functions
      • Replacing Data Types
    • Integration
      • Connecting UI Components
      • Connecting UI to Backend
      • Connecting Multiple Services Together
      • Connecting Cloud Infrastructure (AWS)
    • Separation
      • Abstraction
      • Model View Controller (MVC)
    • Consolidation
      • Combining UI Elements
      • Deduplicating Code Fragments
    • Templating
      • Layouts
      • Schemas
      • Project Structures
      • Content Management Systems
    • Visualization
      • General Styling
      • Visual Referencing
      • Visual Variations
    • Verification
      • Test Classes
      • Logging and Monitoring
      • Automated Testing
      • Synthetic Monitoring
    • Implementation
      • Infrastructure
      • DevOps / Deployment
    • Optimization
      • General Optimization
      • Performance Monitoring
      • Code Review
  • Guidance
    • Business Process
    • Regulatory Guidance
  • Generative Pipelines
  • Troubleshooting
    • Client Side Troubleshooting
    • Server Side Troubleshooting
    • Troubleshooting with AI
    • Documentation
    • Infrastructure Engineering
  • Terminology
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  • Summary
  • Example: Creating brand themed elements through visual referencing
  • Prompt:
  • Response:
  • Discussion

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  1. Subject Knowledge Areas
  2. Visualization

Visual Referencing

Creating brand consistency or collaborations in your applications with visual referencing and generative AI

Summary

Generative AI can be used to apply brand-specific colors and generate themed components for Next.js and Chakra UI applications. By providing context or brand information, developers can create components that match a brand's identity. Additionally, generative AI can create color palettes based on a single color and mood, enabling themed components tailored to specific contexts.

Example: Creating brand themed elements through visual referencing

Prompt:

prompt
Create a McDonald's themed button component in Next.js and Chakra UI. Generate a color palette from a single color based on a happy mood.

Response:

import React from 'react';
import { Button } from '@chakra-ui/react';

const McDonaldsThemedButton = ({ children, ...props }) => {
  const brandColors = {
    primary: '#FFC72C',
    secondary: '#E51837',
  };

  const generatedHappyPalette = {
    100: '#FFD580',
    200: '#FFCA61',
    300: '#FFBF42',
    400: '#FFB523',
    500: '#FFAB04',
  };

  return (
    <Button
      bg={brandColors.primary}
      color={brandColors.secondary}
      _hover={{ bg: generatedHappyPalette[300] }}
      {...props}
    >
      {children}
    </Button>
  );
};

export default McDonaldsThemedButton;

Discussion

Utilizing generative AI to apply colors from context or brand information, and to generate color palettes for themed components in applications, offers several advantages:

  1. Brand consistency: By creating components that adhere to a specific brand's color scheme, developers can maintain consistency across the application and improve brand recognition.

  2. Streamlined component creation: Generative AI can quickly generate themed components that match a brand's identity or mood, reducing manual effort and increasing productivity.

  3. Personalized user experience: Generating color palettes based on a specific mood allows developers to tailor the user experience to different contexts or user preferences.

  4. Maintainable and scalable code: By utilizing generative AI to create components with consistent color schemes and themes, developers can create code that is easier to maintain and scale in the future.

Using generative AI to apply colors and generate themed components in applications enables developers to create components that match a brand's identity, maintain consistency, and provide a personalized user experience while saving time and effort.

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Last updated 3 months ago

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