But that's like one of 12 parts of DSPy, right? So can you break down which parts you're using and how?

Sure.

So, so I'm actually gonna break it down really into, to one conceit, which is DSPy's goal is that you can define your task, what you want your program to do, in a way that it's separate from the how, how that program is implemented.

And you brought up prompt optimizer, so I'll give you a good example, is let's use that task of I want to take all my to-do lists I have to do today, and I want to take all the ones I missed yesterday.

It's a Python framework, so now I have, uh, uh, basically two inputs, current and overdue.

They're basically just a list of items, so it's a list object.

And what do I want out? I want a, you know, descriptive short paragraph write-up that tells me how I did yesterday.

[chuckles]

So

yeah, let's talk about the prompt debt problem.

So one of the things that's wonderful about LLMs, and one of the reasons I've kind of been obsessed with them for a long time, is because they are essentially natural language interfaces that allow us to program computers to get them to do what we want.

And this is a wonderful, magical thing.

It allows us to build prototypes incredibly quickly.

And this is how prototypes today work.

If you're building a prototype of an AI application, you start off with a brief instruction of what you want, you give it to a very large model, you let it cook, and you've got a prototype.

Maybe you could unpack that for