Lenny Rachitsky: Awesome. And I'm hearing also just especially for the very core use case, the most common path people take in your product is where you want to focus. Chip Huyen: Yeah, yeah. Lenny Rachitsky: Okay. There's one more term I want to cover and I want to go a somewhat different direction. RAG? People see this term a lot, R-A-G. What does it mean? Chip Huyen: So RAG stands for Retrieval-Augmented Generations [inaudible 00:32:08] not a specific true generative AI. So the idea is just for a lot of questions, we need context to answer. So I think it came pretty, I think it's from the paper 2017. So someone was like, so they realized it's for a bunch of benchmark. When the question answering benchmarks, they realized it's like, okay, if we give the model informations about the questions, the next answer can be much, much better. So what they do with that is try to retrieve information from Wikipedia. So for question [inaudible 00:32:39], just retrieve that and then put it into the context and answer. It does much better. So I feel like it sounds like a no-brainer, right? I mean, obviously. So I think that's what RAG is, as a simplest sense, it's just providing the model with a relevant context so that it can answer the questions. And it's where things get really more interesting because traditionally, when it started out, RAG is mostly text.
Chip Huyen: So RAG stands for Retrieval-Augmented Generations [inaudible 00:32:08] not a specific true generative AI.