Structured LLM Development
Your teams use ChatGPT and GitHub Copilot. But how can you ensure generated code won't create invisible technical debt?
LLMs are probabilistic, non-deterministic, opaque. How to integrate them without losing architectural control?
The LLM writes code, you verify. But how to progress rather than stagnate? How to avoid becoming a spectator?
LLMs are fundamentally different software components: probabilistic, non-deterministic, opaque by design. Their naive integration into critical systems creates invisible architectural risks that manifest in production. Without a structured framework, AI becomes an uncontrollable dependency rather than a governable component.
Externalize intelligence, internalize control. Intelligence can be probabilistic, control must never be. DC² encapsulates each AI interaction with explicit contracts, verifiable constraints, and external validation points.
DC² structures LLM adoption through 6 phases where humans control strategic and architectural decisions while AI accelerates tactical execution under continuous supervision.
Define architectural vision, strategic decisions and project constraints.
Learn more →Break down the project into User Stories and concrete tasks with success criteria.
Learn more →Create tests and executable specifications before any implementation.
Learn more →Implement minimal code to pass tests under supervision.
Learn more →Improve code quality without changing behavior.
Learn more →Technical, business and content validation for quality assurance.
Learn more →DC² is for professionals and organizations seeking to structure LLM adoption without compromising software quality.
You adopt LLMs in production and seek to structure their use to avoid technical debt.
You guide your teams in AI tool adoption and seek a proven methodological framework.
You design hybrid systems integrating AI components and are looking for a proven governance framework.
You explore AI-assisted development methodologies and seek structured approaches.