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. Intro to GDF-FSE

Why GDF?

Why is GDF needed?

Generative AI tools like ChatGPT are extremely intuitive due to their conversational nature. It raises the question, why does there need to be a methodology or framework?

To answer this, it is important to have the perspective that computing is just a collection of calls and responses, and language is the medium that allows call and response to communicate.

Assume you go to a customer service counter and place an item on the desk. You could say the following things (the call):

  • Say Nothing

  • I want to buy this item

  • I want to return this item

  • I want to buy this item with my American Express, packaged in cardboard box, and delivered to 100 Little St. Big City CO via UPS Next-Day shipping at 3pm EST.

  • Quiero comprar este artículo con mi American Express, empaquetado en una caja de cartón y entregado en 100 Little St. Big City CO a través de UPS Next-Day shipping a las 3:00 p. m. EST.

Based on what you say and what is understood by the person standing behind the counter, you could get a series of responses or a single response. The same is true with a large language model and by understanding what a large language models knows and how it understands requests will allow you to more effectively communicate with it.

The purpose of GDF is not to slow down teams or individuals with yet another process, but to simply provide patterns and principles that can be implemented to whatever extent is efficient and secure.

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Last updated 3 months ago

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