Revolutionizing Tutoring with Retrieval-Augmented Generation (RAG)
Dipti Pawar · Auerbach Publications eBooks · 2025
This chapter introduces Retrieval-Augmented Generation (RAG), a revolutionary approach to tutoring that enhances personalization and engagement. By combining retrieval-based and generation-based models, RAG generates accurate and contextually relevant responses, tailored to individual student needs. The chapter explains the fundamental mechanisms of RAG and uses a tutor analogy to clarify the concept. It outlines the implementation process in AI tutoring systems, from document parsing to response generation, and explores how integrating Large Language Models (LLMs) with RAG techniques creates intelligent, personalized chatbot tutors. The benefits and challenges of RAG, including integration complexity and ethical considerations, are discussed. This chapter demonstrates RAG’s potential to transform tutoring, making it more adaptive and student-centric.