Retrieval-Augmented Generation in Education
Abstract
The chapter aims to assist users in the educational industry who are using RAG (Retrieval-Augmented Generation) by providing its architecture knowledge and foundations. It clarifies the roles of the retriever and generator in designing an Advanced Agentic RAG pipeline and provides a comprehensive overview of the results on the accuracy level. This chapter also provides RAG&s;s evaluation with effective measurement metrics such as precision, recall, faithfulness, and factuality. It also provides an understanding of pre-retrieval enhancement techniques and post-retrieval enhancement techniques. Users can have a deep understanding of how RAG thinks and works from scratch, viewed from a machine&s;s angle.
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