OpenAi enterprise plan especially says that they do not train their models with your data. It's in the contract agreement and it's also visible on the bottom of every chatgpt prompt window.
My personal take is that most companies don't have enough data, and not in sufficiently high quality, to be able to use LLMs for company specific tasks.
But really, in practice, most applications are using the “RAG” (retrieval augmented generation) approach, and actually doing fine tuning is less common.
Wouldn't that depend on what you expect it to do? If you just want say copilot, summarize texts or help writing emails then you're probably good. If you want to use ChatGPT to help solve customer issues or debug problems specific to your company, wouldn't you need to feed it your own data? I'm thinking: Help me find the correct subscription to a customer with these parameters, then you'd need to have ChatGPT know your pricing structure.
One idea I've had, from an experience with an ISP, would be to have the LLM tell customer service: Hey, this is an issue similar to what five of your colleagues just dealt with, in the same area, within 30 minutes. You should consider escalating this to a technician. That would require more or less live feedback to the model, or am I misunderstanding how the current AIs would handle that information?
You can't really maintain authz while fine tuning (unless you do a separate fine-tune for each permission set.) So RAG is the way to go, there.
Isn't this explicitly what RAG is for?