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.

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.

RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data.

5 天之前 · Final Thoughts of RAG in AI. Retrieval Augmented Generation represents a pivotal innovation in artificial intelligence, bridging the gap between static knowledge and dynamic, contextually rich information generation. From enhancing customer support to driving environmental conservation efforts, RAG is not just improving AI’s capabilities ...

2024年6月25日 · RAGBench explainable labels facilitate holistic evaluation of RAG systems, enabling actionable feedback for continuous improvement of production applications. Thorough extensive benchmarking, we find that LLM-based RAG evaluation methods struggle to compete with a finetuned RoBERTa model on the RAG evaluation task.

2025年2月20日 · RAG, or Retrieval Augmented Generation, pairs large language models (LLMs) with tools that pull up-to-date information from external sources. Unlike models that rely on fixed, older data, RAG can access current details. This makes its responses more accurate and relevant—ideal for industries where staying updated matters most.

2025年3月14日 · Infrastructure requirements: Implementing RAG may require the setup of appropriate infrastructure to handle the data and ensure it can be easily searched and accessed by the AI model. Latency issues : The process of retrieving and then generating a response can introduce latency , which might be problematic for real-time applications like ...

2 天之前 · RAG adds a retrieval step that pulls relevant information from external sources using the user’s query. This external data, combined with the original query, is then passed to the LLM. As a result, the model can generate more accurate, detailed, and up-to-date answers.

Retrieval Augmented Generation (RAG) is a revolutionary approach that combines the strengths of knowledge retrieval and text generation to produce more accurate, informative, and engaging text. By leveraging knowledge from a vast database, an AI prompt engineer can enable RAG models to improve accuracy, increase contextual understanding, and ...

2025年3月21日 · The Evolution of RAG. As AI continues to advance, RAG technology is becoming increasingly sophisticated and accessible. Recent innovations, including hybrid search for precise retrieval, multimodal document processing, and quality guardrails, represent significant steps forward in making RAG more powerful, reliable, and comprehensive.