Q Harrison, when you came on the podcast last year, you said that was one of the first papers that you saw when you were getting inspired for Langchain. So maybe give a recap of why you thought it was cool, because you were already working in AI and machine learning. And then, yeah, you can kind of like Intro the paper formally, but what was that interesting to you specifically?
A Yeah. I mean, I think the interesting part was using these language models to interact with the outside world in some form. And, and I think in the paper, you mostly deal with Wikipedia and I think there's some other datasets as well, but the outside world is the outside world. And so interacting with things that weren't present in the LLM and APIs and calling into them and thinking about, and yeah, the, the react reasoning and acting and kind of like combining those together and getting better results. I'd been playing around with LLMs, been talking with people who were playing around with LLMs. People were trying to get LLMs to call into APIs, do things, and it was always, how can they do it more reliably and better? And so this paper was basically a step in that direction. And I think really interesting and also really general as well. Like, I think that's part of the appeal is just how general and simple in a good way, I think the idea was so that it was really appealing for all those reasons.
AI assessment note: “the interesting part was using these language models to interact with the outside world”