How does RAG Improve Accuracy of Generative AI Applications?

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Retrieval-Augmented Generation (RAG) improves the accuracy of generative AI applications by enabling language models to access and retrieve relevant information from trusted data sources such as product catalogs, business documents, databases, and knowledge bases. Instead of relying solely on pre-trained knowledge, the model uses real-time data to generate more accurate, context-aware, and up-to-date responses. As part of advanced generative AI services, RAG helps reduce hallucinations, enhance response quality, and deliver reliable outputs tailored to specific business requirements and user queries.

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