It's important to note that this fine-tuning is what is known as "supervised fine-tuning" where you give the LLM a set of question/answer pairs and it tunes to

It's important to note that this fine-tuning is what is known as "supervised fine-tuning" where you give the LLM a set of question/answer pairs and it tunes to those (see https://huyenchip.com/2023/05/02/rlhf.html https://huyenchip.com/2023/05/02/rlhf.html). This is quite different then fine-tuning the base model or doing RLHF (reinforcement learning from human feedback). My guess is that it could work well to adjust the goal of the LLM, i.e. tell it to behave in a certain way, or do a different task than just being a generalist chat bot. This is quite different than adding knowledge to the bot (known as grounded-generation or retrieval-augmented-generation), which aims to augment the base model with new data (e.g. your confidential data). So in short - I think it's not appropriate for answering questions about a large private knowledge base and GG/RAG is better suited. (if you're interested, I wrote a blog article about this recently: https://vectara.com/fine-tuning-vs-grounded-generation/ https://vectara.com/fine-tuning-vs-grounded-generation/)