Retrieval Augmented Generation or RAG is a technique that enables generative artificial intelligence (Gen AI) models to retrieve and incorporate new information. 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 supplement information from its pre-existing training data. This allows LLMs to use domain-specific and/or updated information (Wikipedia) . It is another fundamental LLM customization technique that is relatively simple, low-cost, and still requires no model modifications.
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