Retrieval-augmented generation is a technique that enables large language models to retrieve and incorporate new information. With RAG, LLMs do not respond to user queries until they refer to a specified set of documents. From Wikipedia
Authors target vector-only limitations through multi-space designs, with gains reported as unreviewed results.
