<ul><li>Retrieval-Augmented Generation (RAG) combines large language models (LLMs) with the ability to retrieve and incorporate relevant external information in NLP.</li><li>RAG addresses the limitations of traditional LLMs, providing more accurate and contextually relevant text by accessing real-time information.</li><li>The adoption of RAG is increasing, especially in customer service, content generation, and question-answering systems.</li><li>Challenges for RAG include data freshness, bias in retrieval, and variations in acceptance and implementation across different regions and cultures.</li></ul>

Understanding the Concept and Mechanisms of Retrieval-Augmented Generation

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