Retrieval-Augmented Generation (RAG)
Definition
Retrieval-augmented generation is the pattern where an AI model fetches relevant material from a knowledge source at question time and answers from what it retrieved, rather than from its training data alone. The model provides the reasoning; the retrieval provides the facts.
RAG is how a general model answers with your business's specifics without being retrained on them. The quality ceiling, though, is set by the retrieval, not the model: if the search returns the wrong five paragraphs, the best model in the world summarises the wrong five paragraphs confidently.
That is why RAG quality work is mostly knowledge-base work: layout, chunking, embeddings and structure, so retrieval answers in one hop instead of five. A knowledge graph raises the ceiling further by giving retrieval relationships to follow rather than just text to match.
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