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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.
Retrieval-augmented generation (RAG) is another approach that enhances LLMs by integrating them with document retrieval systems. Given a query, a document retriever is called to retrieve the most relevant documents.
Procedural generation – Method in which data is created algorithmically as opposed to manually; Retrieval-augmented generation – Type of information retrieval using LLMs; Stochastic parrot – Term used in machine learning
Retrieval-augmented generation, generative AI with the addition of information retrieval capabilities; See also. All pages with titles beginning with Rag;
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.
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Augmented cognition is an interdisciplinary area of psychology and engineering, attracting researchers from the more traditional fields of human-computer interaction, psychology, ergonomics and neuroscience. [1] [2] Augmented cognition research generally focuses on tasks and environments where human–computer interaction and interfaces already ...