Training (or fine-tuning) a custom model to answer domain-specific questions is almost never the right solution. It's complicated, expensive, time-consuming and

Training (or fine-tuning) a custom model to answer domain-specific questions is almost never the right solution. It's complicated, expensive, time-consuming and the results are rarely any good. I have yet to see a demo of someone doing this that I find convincing, at least for adding new knowledge to a model. If you want to teach an LLM to answer questions about private documents you should look into RAG or agentic search - techniques where the LLM can take a user's question and then look for additional information by searching some documents before answering. The good news is that these tricks work reasonably with small models that you can run on your own hardware - even a 4B or 8B model (a few GBs to download) can often handle these cases. But... even then, it's still usually cheaper to pay for the APIs from OpenAI and suchlike. Their API costs are so low that it's hard to save money by running your own model somewhere, since you have to pay to keep it in RAM the whole time while OpenAI share that cost between thousands of users.