In recent years, generative AI models have made leaps in producing human-like text, yet they often struggle with generating factually accurate or contextually grounded responses. Retrieval-Augmented Generation (RAG) models address this by adding a layer of information retrieval, allowing them to pull in specific, relevant data from vast knowledge bases. This retrieval layer complements the generative component, ensuring responses are not only coherent but also well-informed, making RAG models ideal for applications requiring accuracy and depth, such as customer support, content creation, and interactive knowledge systems.

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