See LangGraph's "conditional edges" concept here: https://langchain-ai.github.io/langgraph/concepts/low_level/...
You can see how that "routing function" could include a call to a "Router LLM." And yes, fine tuning is a great method to better improve the routing intelligence of said Router LLM.
Great question btw!
My use case would be fine tuning on technical docs. Specific news, 2 years of blog posts, primary source material, and Twitter explainer thread. I want to gather all the niche information of a topic from the last two years, dump it into this and have an LLM that is a subject-matter expert.
Your use case is better suited to RAG. This is where you retrieve data from a large dataset and inject it into the user's request so the AI model has the context it needs to answer accurately.
But that's not a silver bullet and you would need to spend significant time on chunking strategy and ranking of results to hopefully get a decent response accuracy.
I suppose the Output could be washed by publishing it on the web and having another entity crawl it.
OpenAI doesn't treat anyone else's content any differently, acting like it's a fair game, so why should we care.
Data extraction is a use case that fine-tuned models are fantastic at, so I'm not surprised that OP got good results. That said, I've also found it's pretty easy to beat GPT-4 across many task types if you have a way of getting strong training data. We published some research[1] a week ago where we found that across 4 example tasks spanning creative summarization, question answering, data extraction and classification a fine-tuned Llama 3 8B was able to outperform GPT-4 on 3 of them. The key was to create a repeatable way of generating high-quality training data, which is also addressed in the post.
[1]: https://openpipe.ai/blog/mixture-of-agents