[nfgrep]

The way its typically done, AFAIK, is that you train these big models on a breadth of information, hoping that it picks up on the generalities of the information. In the case of LLMs, things like basic inference, for example. You then take these big, general models and “fine tune” them for specific applications, with specific bits of data. This way, you get things like basic inference, and logic, while still having something that can answer specific questions.

OpenAI offers the ability to fine-tune some of their models: https://platform.openai.com/docs/guides/fine-tuning

There are also other services that will fine-tune an LLM, for a specific domain, for you: https://activechat.ai/build-your-own-chatgpt/