using-loaded-knowledge

Load your project documentation and architecture files into context, then use a pre-response verification protocol that requires the AI to read relevant docs and grep for evidence before answering. This ensures replies are grounded in your actual system, not generated memory.

Enforce a mandatory checklist: inspect auto-loaded context files, read relevant documentation for each question, verify facts with grep or file inspection, and check user profiles before responding. This builds trust by making every claim traceable to your provided sources.

Use loaded-knowledge verification to ground every response in pre-loaded context and official docs. The AI inspects what you've provided, reads relevant sections, and responds only with evidence-based explanations tied to your actual codebase and design.

Yes. Provide your design system documentation and architecture files. The AI follows a pre-response protocol to read your specific docs, verify details with grep, and deliver accurate context-grounded explanations instead of generic responses.

The verification protocol requires the AI to check auto-loaded context and grep for evidence before answering. If documentation is absent, the AI surfaces that gap rather than generating an answer, maintaining trust through transparency about what knowledge is available.

The protocol applies across architecture, design, and project questions. It works wherever you can provide documentation and context files. The AI's verification is independent of your tech stack or domain—it enforces evidence-based responses for any documented system.