Baris Gultekin: recommendation

Transcript context

…So that's interesting. We're sketching out a little bit of a Pareto frontier, so to speak, here, where we have on the simplest but most expensive ed inference time potentially also like rate limit issues. We have our clods and other kind of frontier models. You're in the middle with a snowflake specialist model that is much smaller, does just what it does, but it's still like something that is amortized off over a whole bunch of enterprise customers that you have. Is there, what are you seeing in terms of the other end of that spectrum? Like the, Is there still value in an individual enterprise trying to create its own super specialized model for some of these tasks? Or does that curve stop at the Snowflake scale model? Yeah, it's a good question. So first of all, we partner very closely with all the large language model labs out there. And they have incredible capable models that we use every day. There are some cases where our customers would want something very specific. And this is the case when a customer has large amounts of data, and the use case is something that the model has not seen before, and then they have strict either throughput requirements or cost requirements. Those are the cases where a custom model That is usually based on some of the other large language models out there. Makes sense. So we work with these customers to build custom models for them. But in most cases, a well-tuned RAG solution, text to SQL solution, with the data that they already have, with a large language model that's the frontier model, is usually the go-to scenario. I'm halfway through doing an AMA episode. One of the questions I got was, is fine-tuning really dead? What do you think? So it sounds like you're saying it's not quite dead, but it seems like it's specialized. It's on the decline in your analysis.…

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