Shawn Wang: belief

Transcript context

…But the hard part is how do I join that with the other data? How do I implement RBAC and all this other stuff? And there's a lot of technology that does that. In my observation, database companies tend to succeed when the storage paradigm is closely tied to the execution paradigm. And both of those things need to be rewired to work. Remember that databases are not just storage, but they're also compilers. It's the fact that you need to build a compiler that understands how to utilize a particular storage mechanism that makes the nplusfirst database something that is unique. If you think about Snowflake, it is separating storage from compute and the entire compiler pipeline around query execution hides the fact that separating storage from compute is incredibly inefficient, but gives you this really fast query experience. The arbitrary code is a first-class citizen, which is a very powerful idea, and it's not possible in other database technologies. Arbitrary code is a first-class citizen in my database system. How do I make that work incredibly well? And again, that's a problem which spans storage and compute. Today, the query pattern for vector search is so constrained that it just doesn't have that property. I think I fully understand and mostly agree. I want to hear the opposite view. I think yours is not the consensus view, and I want to hear the other side. I mean, there's super smart people working on this, right? We'll be having Chroma and I think Qtrends on maybe Vespa, actually. One other part of the triangle that I drew that you disagree with, and I thought that was very insightful, was fine-tuning. So I had all these overlapping circles, and I think you agreed with most of them, and I was like, at the center of it all, because you need logging from Ops, and then you need a gateway, and then you need a database with a framework, or whatever, was fine-tuning. And you were like, fine-tuning is not a thing. It's not a business. So there's two things with fine-tuning. One is the technical merits, or whether fine-tuning is a relevant component of a lot of workloads. And I think that's actually quite debatable. The thing I would say is not debatable is whether or not fine-tuning is a business outcome or not. So let's think about the other components of your triangle. Ops slash observability, that is a business thing. Do I know how much money my app costs? Am I enforcing, or sorry, do I know if it's up or down? Do I know if someone complains? Can I retrieve the information about that? Frameworks, evals, databases, do I know if I changed my code? Did it break anything? Gateway, can I access this other model? Can I enforce some cost parameter on it? Whatever. Fine-tuning is a very compelling method that achieves an outcome. The outcome is not fine-tuning, it is can I automatically optimize my use case to perform better if I throw data at the problem? And fine-tuning is one of multiple ways to achieve that. I think the DSPY-style prompt optimization is another one. Turpentine, you know, just like tweaking prompts with wording and hand-crafting few-shot examples and running evals, that's another... Is Turpentine a framework? No, sorry, it's just a metaphor. But maybe it should be a framework.…

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