By combining RAG's data-driven retrieval with MIPRO's prompt optimization, LLMs can be transformed into more accurate and versatile tools for chemical research, capable of delivering reliable predictions even in complex or unfamiliar contexts . This approach holds promise not only for chemistry but also for other scientific domains that require precise, contextually informed data interpretation . Together, RAG and MIPRO can enhance the utility of general-purpose LLMs across a wide range of research applications, reducing the need for specialized models and allowing researchers to leverage LLM technology with greater flexibility and accuracy .