As LLMs and their training data grow in size, their capabilities can seem limitless, however, they cannot be trained on data that does not exist yet. The approach described here takes an LLM incapable of a specific molecular task and makes it substantially more capable through augmented generation and prompt optimization. This approach could allow LLMs to be used as research assistants even when handling data outside of their initial training while maintaining the utility of LLMs in handling language.