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.
Important prerequisite: You should have already completed the Getting Started. It is crucial that you have configured proper privacy settings as outlined in the Project Setup section. This keeps your sensitive information safe when working with AI tools.
Workflow Steps
- Product Requirements - Establish clear and concise requirements for the feature using AI tools to draft and refine.
- Development - Develop the solution iteratively, leveraging AI for implementation.
- Testing - Unit, Functional and End to End testing
- Refactoring - Refactor once feature complete.
- 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.
