Retrieval-augmented generation, or RAG, is a process applied to LLMs to make their outputs more relevant in specific contexts. RAG allows LLMs to access and reference information outside the LLMs own training data, such as an organization’s specific knowledge base, before generating a response—and, crucially, with citations included. This capability enables LLMs to produce highly specific outputs without extensive fine-tuning or training, delivering some of the benefits of a custom LLM at considerably less expense.
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