Mike Conover: preference

Transcript context

…Yeah, you know, when you mentioned you don't wanna re-query like the same thing over and over, a lot of people may say, well, I'll just fine tune this information in the model, you know? How do you think about that? That was one thing when we started working together, you were like, we're not building foundation models. A lot of other startups were like, oh, we're building the finance financial model, the finance foundation model, or whatever. When is the right time for people to do fine tuning versus RAG? Any heuristics that you can share that you use to think about it? So we, in general, I do not, I'll just say like, I don't have a strong opinion about how much information you can imbue into a model that is not present in pre-training through large-scale fine tuning. The benefit of rag is the capability around grounded reasoning. So the, you know, forcing it to attend to a collection of facts that are known and available at inference time, and sort of like materially, like only using these facts. At least in my view, the role of fine tuning is really more around, I think of like language models kind of like a stem cell, and then under fine tuning, they differentiate into different kinds of specific cells, so kidney or an eye cell. And if you think about specifically, like, I don't think that unbounded agentic behaviors are useful, and that instead, a useful LLM system is more like a finite state machine where the behavior of the system is occupying one of many different behavioral regimes and making decisions about what state should I occupy next in order to satisfy the goal. As you think about the graph of those states that your system is moving through, once you develop conviction that one behavior is useful and repeatable and worthwhile to differentiate down into a specific kind of subsystem, that's where like fine tuning and like specifically generating the training data, like having human annotators produce a corpus that is useful enough to get a specific class of behaviors, that's kind of how we use fine tuning rather than trying to imbue net new information into these systems. Yeah, and people always try to turn LLMs into humans. It's like, oh, this is my reviewer, this is my editor. I know you're not in that camp. So any thoughts you have on how people should think about, yeah, how to refer to models?…

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