Retrieval-Augmented Generation (RAG) is an AI framework that enhances text generation by integrating information retrieval into the generative process. Unlike traditional models, RAG retrieves relevant data from external sources or databases to improve the accuracy and relevance of responses. This hybrid approach reduces hallucinations, ensures up-to-date knowledge, and is particularly useful for question-answering systems, research summaries, and content creation. By combining retrieval and generation, RAG bridges the gap between static AI models and dynamic, real-world information needs.

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