AI Development Workflow Overview

This section outlines a streamlined workflow for integrating AI into the development lifecycle. The workflow ensures a consistent approach to developing, testing, and refining AI-driven solutions.

By following this workflow, teams can efficiently integrate AI into their development processes, ensuring high-quality results, maintainable code, and streamlined operations. The workflow emphasizes iteration, validation, and continuous learning to maximize AI's potential in the SDLC.

Workflow Steps

1. Getting Started - A conceptual overview of getting started with AI development workflows.
2. Project Setup - Define the foundational elements for the project, ensuring all necessary files, tools, and conventions are in place.

3. Feature Development Lifecycle

   1. Functional Requirement - Define expected behavior, UI, testable features, and user experience using AI tools to draft and refine.
   2. Technical Specification - Define technical implementation, data models, API endpoints, and backend architecture.
   3. Development - Develop the solution iteratively, leveraging AI for implementation.
   4. Testing - Unit, Functional and End to End testing
   5. Refactoring - Refactor once feature complete.
   6. Documentation - Update all relevant documentation to reflect changes, ensuring clarity and alignment with project goals. Documentation is then used to feedback to the AI tools for future iterations.

4. Learning with AI