Speaker unverified: recommendation

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…We cannot leave this section without talking a little bit about automatic prompts engineering. You have some sections in here, but I don't think it's like a big focus of prompts. The prompt report, DSPy is up and coming sort of approach. You explored that in your self study or case study. What do you think about APE and DSPy? Yeah, before this paper, I thought it's really going to keep being a human thing for quite a while. And that like any optimized prompting approach is just sort of too difficult. And then I spent 20 hours prompt engineering for a task and DSPy beat me in 10 minutes. And that's when I changed my mind. I would absolutely recommend using these, DSPy in particular, because it's just so easy to set up. Really great Python library experience. One limitation, I guess, is that you really need ground truth labels. So it's harder, if not impossible currently to optimize open generation tasks. So like writing, writing newsletters, I suppose, it's harder to automatically optimize those. And I'm actually not aware of any approaches that do other than sort of meta-prompting where you go and you say to ChatsDBD, here's my prompt, improve it for me. I've seen those. I don't know how well those work. Do you do that? No, it's just me manually doing things. Because I'm defining, you know, I'm trying to put together what state of the art summarization is. And actually, it's a surprisingly underexplored area. Yeah, I just have it in a little notebook. I assume that's how most people work. Maybe you have explored like prompting playgrounds. Is there anything that I should be trying?…

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