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
Powered by GitBook
On this page

Was this helpful?

Export as PDF
  1. Subject Knowledge Areas

Specification

Using generative AI to create specifications to build the foundation for you applications

The Specification knowledge area is crucial when working with generative AI, as it helps define the output of a prompt with greater accuracy and detail. In the "Learn through Creation" section, we illustrated how specifying the language, framework, and UI library could guide the development of a bicycle rental application. In many cases, organizations or users of generative AI might possess expertise in specific frameworks, or face financial, compliance, or technological requirements that necessitate using particular tools. In this article, we will explore various prompts and their roles in the Specification knowledge area.

  1. Language Specification: When using generative AI, it is important to specify the programming language you want the generated code to be written in. This ensures that the output aligns with your project's technical requirements and your team's expertise.

Prompt: "Generate a function in Python that calculates the rental cost for a bicycle rental application."

  1. Framework Specification: Specifying the desired framework can help you get the most out of the generated code, as it will be tailored to work seamlessly within the chosen environment.

Prompt: "Create a REST API endpoint for a bicycle rental application using the Express.js framework."

  1. UI Library Specification: Clearly indicating the UI library to be used can help streamline the development process, as the generated code will be compatible with the specified library's components and design system.

Prompt: "Design a user registration form for a bicycle rental application using the Material-UI library in React."

  1. Compliance and Security Specification: Some projects may require adherence to specific compliance or security standards. Specifying these requirements in the prompt can ensure that the generated output complies with relevant regulations and best practices.

Prompt: "Develop a secure authentication system for a bicycle rental application that is GDPR-compliant."

  1. Platform or Device Specification: For projects targeting specific platforms or devices, it's essential to mention these requirements in the prompt to ensure the generated code is optimized for the desired environment.

Prompt: "Create a responsive navigation menu for a bicycle rental application that works well on both desktop and mobile devices."

In conclusion, the Specification knowledge area plays a vital role in obtaining precise output from generative AI. By specifying the language, framework, UI library, compliance requirements, and target platforms or devices, you can guide the AI to generate code that aligns with your project's needs and constraints. Leveraging the Specification knowledge area effectively can lead to more accurate and relevant results, ultimately streamlining the development process and improving the quality of the final product.

PreviousIdeation TemplateNextSpecifying Languages

Last updated 3 months ago

Was this helpful?