Retrieval-augmented generation (RAG) is a cutting-edge
method that empowers generative AI models, such as Large Language Models
(LLMs), to fetch and incorporate factual information from external sources. RAG
empowers these AI models to generate more accurate, relevant, and informative
responses. It helps businesses with enhanced CX, reduced overhead costs, and
improved efficiency overall.
Retrieval
Augmented Generation (RAG) enhances the efficiency and accuracy of LLM
models. This integration of Retrieval Augmented Generation (RAG) empowers
businesses to bypass the time-consuming process of manually shifting through
extensive datasets. Instead, you can leverage the chatbot to quickly retrieve
precise, contextually relevant answers grounded in verifiable source material.
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