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Retrieval-augmented generation (RAG) is a technique that enables generative artificial intelligence (Gen AI) models to retrieve and incorporate new information. [1] 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 ...
Retrieval-augmented generation (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 ...
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.
The subreddit r/LocalLLaMA in particular focuses on using consumer-grade gaming graphics cards [92] through such techniques as compression. That forum is one of only two sources Andrej Karpathy trusts for language model benchmarks. [93] Yann LeCun has advocated open-source models for their value to vertical applications [94] and for improving ...
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
Modify its cognitive architecture to optimize and improve its capabilities and success rates on tasks and goals, this might include implementing features for long-term memories using techniques such as retrieval-augmented generation (RAG), develop specialized subsystems, or agents, each optimized for specific tasks and functions.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
Claude is a family of large language models developed by Anthropic. [1] [2] The first model was released in March 2023.The Claude 3 family, released in March 2024, consists of three models: Haiku, optimized for speed; Sonnet, which balances capability and performance; and Opus, designed for complex reasoning tasks.