Retrieval-augmented generation

Retrieval Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data. This allows LLMs to use domain-specific and/or updated information.[1] Use cases include providing chatbot access to internal company data or giving factual information only from an authoritative source.[2]

  1. ^ Cite error: The named reference Survey was invoked but never defined (see the help page).
  2. ^ Cite error: The named reference AWS was invoked but never defined (see the help page).