By integrating a retrieval step that generates relevant molecular properties and functional group information, this RAG mitigates the risk of hallucinations. This approach addresses the limitations of relying solely on static training data, which may be unavailable for all possible inputs or insufficiently detailed for specialized tasks. By integrating RAG and MIPRO, the LLM's applicability to the chemical task of TPSA prediction was improved, without retraining or fine-tuning the LLM. These results suggest that RAG and MIPRO can significantly improve the utility of general-purpose LLMs in chemical and other scientific research, providing a flexible, scalable solution that enhances prediction accuracy and contextual relevance. This combined approach offers a promising pathway for leveraging LLMs in chemistry and other fields where accurate, context-aware data interpretation is essential. By allowing the model to retrieve relevant information for each query, RAG helps ensure that its predictions are rooted in reliable data.