I fine-tuned an LLM to do Verification IP wiring at a LLM hardware startup. We built the dataset in house. It was quite effective actually, with enough investment in expanding the dataset this is a totally viable application.
I fine-tuned reasoning models (o1-mini and o3-mini) which were already well into instruction-following and reasoning behavior. The dataset I prepared was taking this into account, but it was just simple prompt/response pairs. Defining the task tightly, ensuring the dataset was of high quality, picking the right hyper parameters, and preparing the proper reward function (and modeling that against the API provided) were the keys to success.