Research on Generative AI Chatbot Service based on Academic Data

Recent advances in large-scale language models (LLMs) have brought about significant changes in conversational chatbot systems by achieving excellent results in areas such as text generation, translation, and sentiment analysis. However, LLM-based chatbots have limitations in providing reliable responses due to problems such as hallucination, and this problem needs to be solved even more in areas such as bachelor's data where accuracy and recency are important. This study proposes a design and implementation method of a generative AI chatbot system based on bachelor's data by utilizing the Retrieval-Augmented Generation (RAG) structure, which allows the chatbot to retrieve necessary data in real time from not only internally stored learning data but also external reliable information sources, and generate answers based on them. Through this, the goal is to provide accurate and reliable responses tailored to the characteristics of bachelor's data. It suggests the possibility of implementing a question-answering system that can be safely and efficiently applied even in a non-public data environment by using a generative AI chatbot based on bachelor's data, and through this, allows users to quickly access and utilize the bachelor's information they need.