Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) grounds LLM responses by retrieving relevant documents at query time — a baseline for enterprise context before graphs and MCP.
Retrieval-Augmented Generation (RAG) grounds LLM responses by retrieving relevant documents at query time — a baseline for enterprise context before graphs and MCP.
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RAG limits in regulated workflows
Flat RAG struggles with relational policy questions. Production systems combine RAG with knowledge graphs and live MCP data for governed answers.
Retrieval-Augmented Generation is essential for governed production AI — not optional for regulated deployments
Pilots that skip this discipline typically stall at proof-of-concept
Derisk360 implements through accelerators with embedded Forward Deployed Engineers
Unified context via MCP and knowledge graphs is Layer 01 of the 4-Layer Stack
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Common questions about Retrieval-Augmented Generation
- What is Retrieval-Augmented Generation?
- RAG retrieves relevant documents at query time to ground LLM responses in enterprise knowledge.
- Why does Retrieval-Augmented Generation matter for enterprise AI deployment?
- Retrieval-Augmented Generation reduces deployment risk and determines whether agents reach governed production in regulated environments. Without it, pilots stall and compliance teams block go-live.
- How does Retrieval-Augmented Generation relate to the 4-Layer Intelligence Stack?
- Retrieval-Augmented Generation maps to one or more layers — context, decisions, actions, or outcomes — in Derisk360's architecture for production agentic systems.
- How does Derisk360 implement Retrieval-Augmented Generation?
- Through structured AI accelerators and embedded FDEs who implement retrieval-augmented generation in your VPC — with evaluation and managed operations built in from day one.
- Is this a software product I can licence?
- No. Derisk360 is a services firm. You engage for production outcomes through accelerators and implementations, not shelfware.