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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.
Two-phase process of document retrieval using dense embeddings and LLM for answer formulation. Retrieval-augmented generation (RAG) is a two-phase process involving document retrieval and answer generation by a large language model. The initial phase uses dense embeddings to retrieve documents.
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There is free software on the market capable of recognizing text generated by generative artificial intelligence (such as GPTZero), as well as images, audio or video coming from it. [99] Potential mitigation strategies for detecting generative AI content include digital watermarking , content authentication , information retrieval , and machine ...
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Rag (typography), the ragged edge of a block of text; Recombination-activating gene, encoding enzymes RAG-1 and RAG-2; RAG rating (Red, Amber, Green), a traffic light rating system; Rags (dog) (1916–1936), 1st Infantry Division (United States) mascot in World War I; The Rag (club), alternative name for the Army and Navy Club in London
Knowledge retrieval seeks to return information in a structured form, consistent with human cognitive processes as opposed to simple lists of data items. It draws on a range of fields including epistemology (theory of knowledge), cognitive psychology, cognitive neuroscience, logic and inference, machine learning and knowledge discovery, linguistics, and information technology.
Learned sparse retrieval or sparse neural search is an approach to Information Retrieval which uses a sparse vector representation of queries and documents. [1] It borrows techniques both from lexical bag-of-words and vector embedding algorithms, and is claimed to perform better than either alone.