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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  1. Subject Knowledge Areas

Generation

Generating features and functionality for applications with generative AI

PreviousSpecifying API SpecsNextGenerating UI Elements

Last updated 3 months ago

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The generation knowledge area focuses on how to generate new content within a web application or project. While it could be argued that all of the knowledge areas cover different types of generation patterns, it's essential to have a distinct perspective on each area. The intent and scope of the prompts within each knowledge area differ significantly, making it crucial to understand their unique characteristics.

Consider the following two prompts as examples:

  1. Create a navigation with a list of items for a bicycle rental application.

  2. Locate and update the bicycles navigation item label from "Bike Map" to "Map."

The first prompt falls under the knowledge area. Its intent is to generate a navigation bar, and its scope encompasses an entire navigation bar. The primary focus here is on creating new content from scratch, which serves as the foundation for the application's user interface.

On the other hand, the second prompt belongs to the replacement knowledge area. Its intent is to replace specific content, and its scope is much narrower, focusing on identifying what needs to be replaced. In this case, the emphasis is on refining existing content to improve the user experience or align with changes in the application's functionality.

The distinction between these two knowledge areas is essential because it highlights the difference in their approaches and considerations. While the generation knowledge area is concerned with creating new content, the replacement knowledge area is more focused on updating and enhancing existing content. By understanding the unique aspects of each area, developers can more effectively leverage them to create robust, maintainable, and user-friendly applications.

In conclusion, the generation knowledge area serves as a valuable resource for creating new content and building the foundation of an application. By recognizing the distinct characteristics of each knowledge area, developers can better tailor their approaches to meet the specific needs and goals of their projects. By embracing these unique perspectives, teams can create more efficient, maintainable, and engaging web applications that drive success in today's competitive digital landscape.

generation