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Rag Retrieval-Augmented Generation is a powerful technique that combines large language models with external knowledge sources. Instead of relying solely on pre-trained data, RAG retrieves relevant documents in real time and feeds them into the model for more accurate, up-to-date responses. This boosts factual accuracy, reduces hallucinations, and enables dynamic, context-aware outputs, making it ideal for enterprise AI, customer support, and research-heavy applications. It's the next step in trustworthy generative AI.