MIPRO iteratively identifies the optimal combination of examples and instructions, creating a prompt that enables the model to consider functional group contributions, functional group details, and additive rules when making predictions. The addition of MIPRO's optimized prompts allowed the model to better interpret molecular structure and contextual details, such as functional group contributions which are essential for accurate predictions. Our ablation studies showed that removing specific prompt components led to increased error rates, confirming the importance of each element in minimizing model hallucinations. For instance, omitting functional group descriptions or atom counts resulted in poorer alignment, with RMSE rising to 28.95 when both elements were removed. These findings underscore the necessity of detailed molecular context in LLM prompts, when property predictions depend on molecular features.