Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation — RAG — is the architecture that lets a language model answer questions about a specific business by retrieving relevant documents from that business's own knowledge base before generating the response. The model is pre-trained on the open internet; RAG grafts the firm's private knowledge onto that general intelligence at query time.

The mechanics are straightforward. The firm's documents — policies, contracts, prior decisions, customer interactions, technical specs — are processed into a vector index that captures the meaning of each chunk. When a question arrives, the retrieval step finds the chunks most relevant to the question. Those chunks are then handed to the language model along with the question, and the model generates an answer grounded in the retrieved material — with citations back to the source.

RAG matters because it solves the two failure modes of using a general-purpose chat assistant inside a business. Without it, the model either hallucinates (invents plausible-but-wrong specifics about the firm) or admits ignorance (refuses to answer because it doesn't know your specifics). RAG fixes both: the retrieval grounds the answer in real source material, and the citations let the human verify before acting on the response.

The most useful applications inside a mid-market firm are HR policy lookup, customer support drafting, sales enablement (proposals and product Q&A), compliance advisory, and engineering onboarding. Each of these is a use case where the answer depends on the firm's specifics and the business value of getting it right at speed is high. RAG turns the question "does anyone here know X" from a slack thread that took two hours into a query that takes ten seconds with a citation attached.