The technical heart of the framework consists of five specialised components that work together to ensure quality, security, and maintainability. Each component features structured methodologies with clear acronyms (S.C.A.F.F., V.E.R.I.F.Y., S.H.I.E.L.D., D.O.C.S., and R.E.F.A.C.T.) to make implementation more accessible and consistent.
The framework provides tailored implementation approaches for different contexts, from individual developers to enterprise organizations. Each guide offers practical steps, timelines, and tools suited to the specific needs and constraints of different team structures and organisational scales.
Effective collaboration is essential when working with AI tools. The C.O.D.E.S. model and related practices ensure that knowledge is shared, quality is maintained, and teams develop balanced skills even as AI handles more implementation details. These approaches transform AI-assisted development from a potentially isolating practice to a collaborative one.
The framework includes recommendations for practical tools and technologies that support implementation. These resources range from local LLM solutions for secure environments to specialized security scanning tools and prompt management systems that help teams organize and improve their AI interactions.
As AI technologies and software development practices continue to advance, the framework itself must evolve. This section outlines how the community can contribute to the framework, how versions are managed, and the roadmap for future development, ensuring the framework remains relevant and effective.
