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
    • Protecting Application Security
    • Protecting Intellectual Property
    • Protection Stores
    • AI Security Assessments and Penetration Testing
    • 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
      • Converting Libraries
    • 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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  • Using Prompts for Style Specification
  • Generating Component Variations
  • Discussion:

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  1. Subject Knowledge Areas
  2. Visualization

Visual Variations

Getting creative with visual variations and generative AI for uniquely styled user interfaces

In web design, creating visually appealing components that match a specific style or theme is essential for a consistent user experience. Generative AI can help developers create variations of components based on the desired style, such as modern, corporate, or creative, while adhering to other style rules specified using prompts. In this article, we will explore how to use generative AI in conjunction with Next.js and Chakra UI to generate visually distinct component variations.

Using Prompts for Style Specification

To use generative AI effectively, you must provide clear and concise prompts. Recall the command-target-specifier (CTS) pattern, prompt context, prompt chaining, prompt operators, and prompt stores discussed earlier. These concepts can be leveraged to provide style specifications for your components.

For example, you might use the following prompt:

prompt
Generate a modern button component in Next.js and Chakra UI that has a bold font, rounded corners, and a primary color of #0070f3.

By incorporating the CTS pattern, you can provide more specific context and design requirements for the generative AI to create the desired component.

Generating Component Variations

With the style prompts in place, generative AI can create visual variations of a component based on the specified style. Let's consider three different styles: modern, corporate, and creative.

  1. Modern: This style often features bold typography, strong colors, and clean lines. For a modern-styled button component, you might use a prompt like:

prompt
Generate a modern button component in Next.js and Chakra UI with a primary color of #0070f3, bold font, and rounded corners.
  1. Corporate: Corporate designs tend to be more conservative, with a focus on clarity and professionalism. For a corporate-styled button component, the prompt might be:

prompt
Generate a corporate button component in Next.js and Chakra UI with a primary color of #0a66c2, regular font weight, and slightly rounded corners.
  1. Creative: Creative styles often involve unconventional layouts, vibrant colors, and unique typography. For a creative-styled button component, consider using a prompt like:

prompt
Generate a creative button component in Next.js and Chakra UI with a gradient primary color from #ff0080 to #7928ca, bold font, and irregular border shapes.

The generative AI will use the provided prompts to create the corresponding component variations, adhering to the specific style requirements mentioned.

Discussion:

Generative AI can be a powerful tool for creating visually appealing components in different styles while still matching other specified style rules.

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Last updated 3 months ago

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