Shawn Wang: belief

Transcript context

…I guess mine would be, let humans be good at doing human things. Let machines be good at doing machine things and let machines be good at doing machine things and help humans be good at doing human things. And like, if you don't do that, then you're gonna be building something that's either not useful or it's very scary. So yeah, get machines helping humans, not the other way around. Get machines helping humans. All right. With that, I think we're all gonna open up to questions. We're gonna toss you the mic. So we have a custom query engine. So it's not SQL or anything that we're generating. We're generating a custom query output. So I guess the types of questions range. So things like chart type, are we doing a segmentation chart, a line chart or are we doing a funnel chart? You know, the number goes down over time or up over time or between a conversion between two events and there are various other types or metrics or, and then there's also the name. What should we name this chart that answers this question? So the way that's implemented in practice, you could use something like Lang chain to sort of chain these things together. But in our experience, I think Lang chain's a great tool for certain things and definitely really great for prototyping, but we found it quite restrictive. So we've ended up building sort of an internal, it's a very, very small wrapper, internal, we use TypeScript as well, framework that allows us to basically just write in code and infer within what we call a transaction, an inference transaction, which gets monitored as one, but then also all the individual inferences within it get monitored. So it's a bit like when you're writing a database transaction with most sort of, at least in the node ecosystem, the JavaScript ecosystem, where you sort of get a transaction object that you can operate on, and then you return your, or you return, you sort of commit your transaction. So we've got an interface like that, so we can just write pure TypeScript, await this response or await these responses. And then we've got a switch case. If it's a segmentation chart, go and do these with these queries. And then each of those inferences can be a different model. So we think in the future, maybe we have one query where we have some GPT-4 responses. We want some text responses. Maybe we also want to generate an image from that same query together, and then that gets bundled. So I don't know if that answers your question. Right now, everything's GPT-3.5. We're moving around, and I think probably for some of the prompts, we'll use something like DaVinci. Some we might use GPT-4. Some we'll be using internal ones. And we also want to be able to degrade gracefully if a customer has told us they don't want us to send anything to OpenAI, then we can degrade to some internal models that maybe are some of the open source models that have been trained on smaller datasets.…

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