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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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A simpler form of tool use is RAG, retrieval-augmented generation: the augmentation of an LLM with document retrieval. Given a query, a document retriever is called to retrieve the most relevant documents.
Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data.
The Rag (club), alternative name for the Army and Navy Club in London; Ragioniere or rag., an Italian honorific for a school graduate in business economics; Retrieval-augmented generation, generative AI with the addition of information retrieval capabilities
Flux is a series of text-to-image models. The models are based on a hybrid architecture that combines multimodal and parallel diffusion transformer blocks scaled to 12 billion parameters. [8]
The inverse of mathematical question answering—mathematical question generation—has also been researched. The PhysWikiQuiz physics question generation and test engine retrieves mathematical formulae from Wikidata together with semantic information about their constituting identifiers (names and values of variables). [20]