Retrieval-Augmented Generation, known as RAG, harnesses the capabilities of LLMs (Large Language Models) to offer an effective method for accessing external information. LLMs comprehend the inquiry and leverage the contextual details in the given external materials to formulate a response on the subject. This process essentially bridges the gap between the vast general knowledge of an LLM and the specific, often niche, information contained within a user’s own documents. The aim is to create a
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