Retrieval-Augmented Generation (RAG)
RAG is an AI technique that retrieves relevant text from approved documents and uses it as the basis for a language-model answer, rather than generating from model memory alone.
Definition
Retrieval-Augmented Generation (RAG) is an architecture for generative AI systems that separates knowledge from reasoning. At query time the system first retrieves relevant passages from a trusted knowledge source — a document store, vector database, or search index — and then passes those passages to a large language model as context for generating the answer. The model is instructed to answer only from the retrieved passages and to cite them. If nothing relevant is retrieved, a well-designed RAG system returns "I don't know" instead of inventing an answer.
Why it matters
RAG is a common architectural choice for enterprise AI because it can improve knowledge freshness and reduce unsupported answers. It does not eliminate hallucinations: retrieval can fail and a model can still misread or overstate a source. Citations, evaluation, access controls, and human review remain important.
How Volentis.ai handles it
Every Volentis.ai agent uses a RAG pipeline over a customer's approved documents. The retrieval step is scoped to the role-based permissions of the asking employee — an agent only retrieves from documents the user is authorized to see. Every generated answer includes a citation pointing to the exact source passage, and the full retrieval-plus-generation trace is logged for audit.