Retrieval-Augmented Generation (RAG) is an architecture where a system searches your private knowledge, documents, tickets, wikis, databases, and feeds the most relevant passages into a large language model before it answers. Instead of relying only on what the model memorized during training, responses are grounded in evidence you control, often with citations so humans can verify claims.
Production RAG is more than a vector database demo. Chunking strategy, hybrid search, access-control filters, evaluation harnesses, and re-indexing pipelines determine whether answers stay accurate as content changes. Codoop builds enterprise RAG so assistants respect tenant boundaries, cite sources, and remain operable inside your cloud, not as a fragile chatbot wrapper.
Related: /ai-services/enterprise-rag