Introduction

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Retrieval-Augmented Generation (RAG) is a hybrid approach combining the power of large language models (LLMs) with external knowledge sources. It enhances the capabilities of LLMs by integrating a retrieval mechanism that fetches relevant documents or information from a predefined dataset or the web, augmenting the model's generative process. This is particularly useful for tasks requiring up-to-date or domain-specific information that might not be part of the model's training data.

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