These efforts underscore the progress being made with specialized chemical LLMs and instruction-tuned models, but they come with limitations. Developing or fine-tuning models on dedicated chemical datasets requires substantial computational and energy resources ] and domain-specific expertise . Furthermore, once models are fine-tuned for a specific chemical application, their generalizability may suffer, and their adaptability to other domains or newly emerging chemical knowledge can become constrained . Therefore, there is a need for time-of-prompt solutions that can enhance the accuracy of LLM predictions at inference time-without requiring extensive retraining or fine-tuning . Such techniques would allow LLMs to be applied to a wider range of chemical tasks, even in cases where the model's pre-existing knowledge may be incomplete or out-of-date.