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Retrieval-augmented generation - Wikipedia

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Retrieval augmented generation (RAG) is a type of information retrieval process. It modifies interactions with a large language model (LLM) so that it responds to queries with reference to a specified set of documents, using it in preference to 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] The RAG process is made up of four key stages. First, all the data must be prepared and indexed for use by the LLM. Thereafter, each query consists of a retrieval, augmentation and a generation phase.[1] The data to be referenced must first be converted into LLM embeddings, numerical representations in the form of large vectors. RAG can be used on unstructured (usually text), semi-structured, or structured data (for example knowledge graphs).[1] These embeddings are then stored in a

Retrieval-augmented generation - Wikipedia Jump to content From Wikipedia, the free encyclopedia Type of information retrieval using LLMs Retrieval-augmented generation ( RAG ) is a technique that enables large language models (LLMs) to retrieve and incorporate new information from external data sources. [ 1 ] With RAG, LLMs first refer to a specified set of documents, then respond to user queries. These documents supplement information from the LLM's pre-existing training data . [ 2 ] This allows LLMs to use domain-specific and/or updated information that is not available in the training data

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