This paper proposes an innovative approach to address the challenge of semantic interpretation of principal components generated by the widely used Principal Component Analysis (PCA) on complex datasets. We propose a novel method that incorporates large language models (LLM) into the interpretation process. This approach aims to bridge the gap between the statistical complexity of PCA and the practical applicability of latent variables. Our initial results demonstrate the effectiveness and promising precision that this approach can achieve in translating complex mathematical relationships into contextually rich semantic interpretation. This work represents a significant step towards improving interpretability in data science, and a valuable resource to facilitate informed decision making and understanding in diverse research and data analysis contexts.