Definition
Retrieval-Augmented Generation (RAG)
A technique that enhances LLM responses by retrieving relevant documents from an external knowledge base and including them in the model's context.
In Depth
RAG addresses the fundamental limitation of LLMs: their knowledge is frozen at training time. By retrieving relevant documents at query time and injecting them into the prompt, RAG gives the model access to current, domain-specific information. In agent systems, RAG is used to ground agent responses in your company's actual data — support docs, product specs, policies — rather than relying on the model's general training. This dramatically reduces hallucination and makes agents trustworthy for production use.
Related Terms
Build production AI agents with EigenForge
Join the Waitlist