2025年2月10日 · Retrieval-augmented generation (RAG) enhances natural language processing by combining retrieval and generation models to provide accurate, contextually relevant, and up-to-date responses using external data sources.

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.

2024年11月12日 · RAG is a method that combines the strengths of traditional information retrieval systems with the generative capabilities of LLMs. It works by: Retrieval: When a user query is received, the system searches a large, up-to-date database or corpus for relevant documents.

2024年12月18日 · Retrieval Augmented Generation (RAG) is an architecture that augments the capabilities of a Large Language Model (LLM) like ChatGPT by adding an information retrieval system that provides grounding data.

2025年1月31日 · So, What Is Retrieval-Augmented Generation (RAG)? Retrieval-augmented generation is a technique for enhancing the accuracy and reliability of generative AI models with information fetched from specific and relevant data sources.

Learn about retrieval-augmented generation (RAG), an AI framework that combines retrieval-based and generative models to produce more accurate responses.

2025年3月14日 · Retrieval Augmented Generation (RAG) is a technique that enhances LLMs by integrating them with external data sources. By combining the generative capabilities of models like GPT-4 with precise information retrieval mechanisms, RAG enables AI systems to produce more accurate and contextually relevant responses.

2023年8月22日 · Retrieval-augmented generation (RAG) is an AI framework for improving the quality of LLM-generated responses by grounding the model on external sources of knowledge to supplement the LLM’s internal representation of information.

5 天之前 · RAG enhances the tremendous language capabilities of LLMs by incorporating external sources of truth, reducing hallucination rates. Plain old non-RAG LLMs have so-called “parametric memory.” The LLM training process embeds knowledge contained within the training set into the model’s output parameters.

6 天之前 · Retrieval augmented generation (RAG) encompasses a family of systems that extend conventional language models, large and otherwise (LLMs), to incorporate context based on retrieved knowledge from a document base, thereby leading to more truthful and relevant responses being generated upon user queries.. In this context, LangChain attained particular …