RAG
Explain how retrieval-augmented generation works, and the roles Embedding, vector databases, and resorting play in enterprise knowledge base Q&A.
RAG is retrieval-augmented generation, allowing large models to look for evidence from external sources before answering, rather than relying solely on memory. Here, we explain the basic process of RAG, as well as what Embedding, vector databases, and reorder models each handle in knowledge base Q&A, helping teams avoid detours in search and slicing.
