context-driven-development

You can maintain project context by defining persistent artifacts like product.md, tech-stack.md, and workflow.md alongside your code. This approach ensures AI alignment and team coordination by treating context as a versioned source of truth.

The best way to align AI workflows is to initialize a context-driven development process by verifying existing context artifacts and starting an evaluate-loop. This tracks context evolution and guides decisions for both greenfield and brownfield projects.

Structure documentation by creating separate markdown artifacts: product.md for goals, tech-stack.md for technology choices, and workflow.md for processes. Enforce a frontmatter with name and description to maintain a single source of truth.

No specific dependencies are required to manage AI artifacts. The workflow operates independently to define, validate, and synchronize context files, supporting optional artifacts like scripts and references loaded during operation.

AI loses project context because it lacks a persistent, versioned artifact structure. By applying a context-driven workflow with lifecycle management, you synchronize context artifacts across tracks to ensure consistent AI behavior.

Yes, you can use markdown artifacts for team onboarding. Context-driven development helps new teammates understand context relationships and maintains synchronized processes through structured lifecycle management across tracks.