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Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a Large Language Model (LLM), in such a way that it references an authoritative knowledge base outside of its training data sources before generating a response. Hence RAG extends the capabilities of LLMs to specific domains using an internal knowledge base without the need to retrain the model.