AI Development Workflow

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. Product Requirements - Establish clear and concise requirements for the feature using AI tools to draft and refine.
   2. Development - Develop the solution iteratively, leveraging AI for implementation.
   3. Testing - Unit, Functional and End to End testing
   4. Refactoring - Refactor once feature complete.
   5. 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.