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Understanding Retrieval-Augmented Generation by shipster6699 is a document available to read on EtoBox.

The document discusses Retrieval-Augmented Generation (RAG), a framework that combines generative capabilities of large language models (LLMs) with external knowledge to improve information retrieval in organizations. It highlights the limitations of traditional systems and the benefits of using CAs powered by AI to access organizational data while reducing the risk of unreliable outputs, termed hallucinations. The paper also reviews enhancements to RAG architecture and outlines various use cases and challe

Author

shipster6699

Language

EN