Baris Gultekin: prediction

Transcript context

…Yeah. Okay. Let's go back more toward the technical side for a minute. We kind of went debut in text to SQL. Let's do the same thing for RAG. So we've got all these unstructured, vast amounts of data out there. They're getting loaded into platforms. They're getting metadata synthetically created by AIs coming through and just processing them suite by suite. How well is that working and what is actually key to making it work? We've been through eras of chunking strategy is really important, or I've done episodes on graph databases and entity recognition and figuring out various ways to traverse the entity graph that, and obviously it's it's going to be quite distinct for each enterprise with all the different entities that they're going to have that nobody else has. What is really driving results in that RAG paradigm today? Yeah, at Snowflake, we've been actually very fortunate. We acquired a company that I came with called Niva, which was a web-scale search engine. I was a user. All right, awesome. So we brought that technology into Snowflake to build out our search and RAG solutions. And there, basically what determines quality is the quality of the embedding model that you're using. Of course, there is more and more sophisticated chunking strategies of what you're indexing. And then there is kind of other layers like the hybrid search and the re-ranker that you build on top of it and so forth. Increasingly, a core part of it is also to be able to understand complex documents. PDFs are messy, you have images, you have tables, you have multiple columns in a page and so forth. So being able to handle all of this extract information really accurately. figuring out which model, embedding model to use, whether you should use a multimodal embedding model or a text embedding model and so forth. So all of those are incredibly important, but increasingly, we're getting to a point where you can automate many of these things and then reduce the complexity so that a lot of what we used to require practitioners to do can be relatively automated at this point. And now you're getting to a point where more interesting opportunities get unlocked. So for a company like Snowflake, for instance, being able to do what we're calling analytical, agentic document analytics is something that is possible to do. So what I mean by that is, let's say that you have thousands of PDFs and there is information in it, Let's say you have quarterly results over the last 10 years. So being able to say, what's the average revenue over the last 10 years? And if that is in multiple different documents, being able to extract all of that and then do analytics on it is now possible. So overall, I think RAG is both getting increasingly higher in quality and also simpler to build and increasingly more and more powerful to handle some of the new agentic use cases. Would it be a fair distillation of what you've said there that you're trending more toward more powerful models? Like a project that I've been involved with recently is built around understanding, often scanned on like a physical scanner forms that are associated with the sale of a car from either a dealer to a person or person to person. These things, of course, have to get filed with the state and reviewed and they're super messy and whatever. So working a little bit with a company that's using AI to automate that. In that context, I've really seen a pretty substantial simplification where 18 months ago it was like, I You might need your specialist embedding model here and your kind of table extractor model there and all this kind of deep specialization, often not super large models, but like really dialed in on these use cases. And now I would say today, Claude four or five Opus or Gemini three mostly just solve the problem off the shelf in terms of understanding those documents at a higher cost, certainly inference wise, but definitely a lot lower cost in terms of AI engineering time. Am I right to say you're seeing the same trend, the less specialized models?…

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