The business applications of large language models (LLMs) have been expanding rapidly, with new technologies being introduced continuously. Recently, Retrieval-Augmented Generation (RAG) has gained attention, requiring an understanding of vector databases for effective utilization. However, for individuals without specialized technical expertise, the cognitive burden can be high, making practical implementation challenging. In this study, we focus on a subset of soil classification names used in geotechnical engineering and applied geology. We employ BERT to visualize the relationships among these terms by mapping them in three dimensions. Additionally, ChatGPT-4o and ChatGPTo3-mini-high were used to infer the meanings of the three axes, prompting AI to reconsider the relationships incorporating geotechnical knowledge. As a result, we propose a method that enables dynamic updates to soil classification correlations without incurring high development costs.