Together, RAG and MIPRO present a powerful solution for improving LLM performance. RAG addresses the issue of outdated or incomplete information by grounding the LLM's responses in current, high-quality data sources, ensuring that predictions are accurate and contextually relevant. MIPRO complements this by optimizing the prompt structure, allowing the LLM to interpret and utilize retrieved data more effectively through well-designed instructions and examples. Here, as an example of this approach, I describe a method for predicting TPSA that combines RAG and MIPRO using a commercially available LLM, ChatGPT-4o-mini. In tandem, these approaches enabled the LLM to make accurate, data-driven predictions at inference time, enhancing its reliability without fine tuning the weights of the base model. This approach reduced the root mean squared error (RMSE) from 59.41 for prediction using the GPT-4o-mini LLM directly to 7.44 RMSE when MIPRO and RAG were employed for predictions on a set of random molecules. The individual contribution of the various elements of this approach is described below.