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Retrieval Augmented Generation

Retrieval Augmented Generation (RAG) is a hybrid AI approach that combines a neural language model with a retrieval system. It enhances language generation by dynamically fetching relevant external information during the generation process. This technique allows the model to incorporate up-to-date, specific knowledge beyond its initial training data. RAG is particularly effective for tasks requiring deep, context-aware responses and factual accuracy.