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Jun 11, 2024 · Retrieval-augmented generation (RAG) is an innovative approach in the field of natural language processing (NLP) that combines the strengths of retrieval-based and generation-based models to enhance the quality of generated text.
Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response.
Dec 14, 2023 · If you’re looking for a non-technical introduction to RAG, including answers to various getting-started questions and a discussion of relevant use-cases, check out our breakdown of RAG here.
Sep 11, 2024 · Retrieval Augmented Generation (RAG) is a transformative concept in AI and NLP. Additionally, by harmonizing retrieval and generation components, RAG addresses the limitations of existing language models and paves the way for more intelligent and context-aware AI interactions.
Jan 30, 2024 · What is RAG? RAG, or Retrieval Augmented Generation, is a technique that combines the capabilities of a pre-trained large language model with an external data source.
Retrieval augmented generation (RAG) is a type of generative artificial intelligence that has 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 in preference to information drawn from its ...
RAG definition: 1. a torn piece of old cloth: 2. clothes that are old and torn: 3. a newspaper or magazine that…. Learn more.
Nov 15, 2023 · Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources.
Apr 12, 2024 · Despite the popularity of RAG, it doesn’t work well in all contexts. In this article, we’ll explain step-by-step how to build RAG pipelines effectively, explore the limitations of RAG, and how to address these limitations. Let’s get started!
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