How to Build an AI Tutor that Can Adapt to Any Course and Provide Accurate Answers Using Large Language Model and Retrieval-Augmented Generation

arXiv:2311.17696v3 Announce Type: replace
Abstract: This paper proposes a low-code solution to build an AI tutor that leverages advanced AI techniques to provide accurate and contextually relevant responses in a personalized learning environment. The OpenAI Assistants API allows AI Tutor to easily embed, store, retrieve, and manage files and chat history, enabling a low-code solution. Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) technology generate sophisticated answers based on course-specific materials. The application efficiently organizes and retrieves relevant information through …

abstract adapt advanced advanced ai ai techniques api arxiv assistants build code course cs.cl environment language language model large language large language model low low-code openai paper personalized replace responses retrieval retrieval-augmented solution tutor type