This study provided a simple example of a molecular property prediction that allowed a detailed examination of strategies to reduce LLM hallucinations in scientific applications. The results demonstrate the effectiveness of both RAG and MIPRO individually and in combination to improve the accuracy and reliability of LLMs in predicting molecular properties, a critical aspect of drug research. By augmenting LLMs with both external data retrieval and optimized prompt structures, we observed a significant reduction in prediction errors. Specifically, the fully optimized model achieved an RMSE of 7.44, closely aligning with calculated TPSA values and outperforming models that used only a simple prompt or incomplete prompt components.