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This survey aims to consolidate current knowledge in RAG research and serve as a foundation for the next generation of retrieval

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While Large Language Models LLMs are revolutionary their deployment is constrained by inherent limitations such as factual

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RAG addresses critical limitations of standalone LLMs by enriching parametric knowledge with external evidence

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Background Retrieval augmented generation RAG aims to reduce hallucinations and outdated knowledge by

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To address the limitations in knowledge acquisition for generative models Retrieval Augmented Generation

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In particular RAG introduces the information retrieval process which enhances the generation process by retrieving relevant objects

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We identify and discuss the central technologies integral to the RAG process specifically focusing on the aspects of Retrieval

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Specifically we examine and compare several quantifiable metrics of the Retrieval and Generation components such

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In this paper we comprehensively review existing research that integrates RAG into educational scenarios We first