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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.
Retrieval augmented generation (RAG) is an architecture for optimizing the performance of an artificial intelligence (AI) model by connecting it with external knowledge bases. RAG helps large language models (LLMs) deliver more relevant responses at a higher quality.
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. This approach combines the generative power of LLMs like GPT-3 or GPT-4 with the precision of specialized data search mechanisms, resulting in a system that can offer nuanced ...
Oct 30, 2024 · Retrieval-augmented generation, or RAG, is a process applied to large language models to make their outputs more relevant for the end user. A golden outline of a speech bubble is filled with a jumble of colorful, balloon-like spheres. The spheres vary in size, color, and texture, and the image is set against a light-gray background. In recent ...
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 ...
Sep 19, 2023 · Key Takeaways. RAG is a relatively new artificial intelligence technique that can improve the quality of generative AI by allowing large language model (LLMs) to tap additional data resources without retraining.
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.
Sep 21, 2023 · Retrieval-augmented generation (RAG) is an advanced artificial intelligence (AI) technique that combines information retrieval with text generation, allowing AI models to retrieve relevant information from a knowledge source and incorporate it into generated text.
RAG is an architectural pattern in generative AI designed to enhance the accuracy and relevance of responses generated by Large Language Models (LLMs). It works by retrieving external data from a vector database at the time a prompt is issued.
