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

• URL: http://arxiv.org/abs/2311.17696v3
• Date: Fri, 21 Jun 2024 09:29:07 GMT
• Title: How to Build an AI Tutor that Can Adapt to Any Course and Provide Accurate Answers Using Large Language Model and Retrieval-Augmented Generation
• Authors: Chenxi Dong,
• Abstract summary: The OpenAI Assistants API allows AI Tutor to easily embed, store, retrieve, and manage files and chat history. The AI Tutor prototype demonstrates its ability to generate relevant, accurate answers with source citations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 vector embedding and similarity-based retrieval algorithms. The AI Tutor prototype demonstrates its ability to generate relevant, accurate answers with source citations. It represents a significant advancement in technology-enhanced tutoring systems, democratizing access to high-quality, customized educational support in higher education.

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