context-driven-development

Scaffolding new projects with foundational context artifacts like product.md, tech-stack.md, and workflow.md establishes structured project documentation. This ensures clear understanding of product goals, technology choices, and team processes from day one for both AI alignment and team collaboration.

Yes, you can extract context from existing codebases to support brownfield development. This approach analyzes your current codebase to generate initial documentation, capturing the existing technical stack and development workflows to establish structured project artifacts for AI alignment.

The best way to scaffold documentation is by creating essential context artifacts like product.md, tech-stack.md, and workflow.md from day one. This establishes structured project documentation, ensuring clear understanding of product goals, technology choices, and team processes for AI alignment and team collaboration.

Validating artifact consistency checks that your product vision, technical stack, and development workflow documents are coherent before implementation begins. This ensures documentation alignment, preventing discrepancies between the structured project context and the actual codebase during development.

Synchronizing documents throughout the project lifecycle updates your context artifacts as the codebase evolves. This maintains up-to-date product vision and technical stack documentation, ensuring AI interactions and team collaboration always reflect the latest development workflows and project state.

Yes, this context management approach supports both greenfield and brownfield development. It scaffolds new context artifacts for greenfield projects and extracts context from existing code for brownfield projects, ensuring structured documentation and AI alignment regardless of the project lifecycle stage.