What Large Language Models (LLMs) can bring to the realm of science and engineering is a question that we have been pondering since the advent of ChatGPT. There is no question that LLMs has already shown its superb potential as a literature reviewer, all one needs is to feed more literature with full content to it. Then, what else? Beyond the role of experimentalist's assistant, which we have just developed in the form of CRESt, we envision it will also play a transformative role in at least the following three dimensions: Instrument coach. Presently, researchers must comprehend the theoretical basis of any technology they wish to utilize, along with the specific operations (sometimes empirical rules or "tricks") of an individual instrument, which can vary significantly from one manufacturer to the other. This latter requirement involves a non-trivial effort, e.g. a series of training sessions for a shared facility or reading a 200-page manual for a group-owned instrument, but is it truly indispensable? We foresee that, in the imminent future, researchers will merely need to articulate their needs in plain language, and LLMs will translate these into the optimal parameter settings (which is what an instrument specialist is doing now). Upon request, the exact part in the manual book can be referred to for the users to further investigate into. Technically, this can be made possible by appropriately fine-tuning an LLM base model by the vendor, which can be done today.