![“I don't think voice is necessarily low [effort]. It's more like how much information is in the prompt. You can um and uh and add some sentences and be like, oh, actually I changed my mind, in the middle of the prompt, and it will be able to follow that perfectly. … For a lot of people, it's just way easier to talk than to type. And if that gets more information out of you, that's better.” — Thariq Shihipar, Latent Space](https://fls-a263efba-c0f9-4b9c-9883-33f77640c80c.laravel.cloud/quote-cards/fypj-sppx-epf7-9b036d56a959.png)
swyx said half his prompts are two minutes of rambling into voice dictation, and asked whether that's worse than a structured, PRD-style prompt. Thariq, who works on Claude Code at Anthropic, said the format barely matters to the model. What counts is how much information gets into the prompt, and most people get more out by talking than by typing.
Yeah, yeah, exactly.
One thing I go back and forth on is I feel like the way I prompt half the time, let's say I use voice. Guys have voice, other people have voice. That is the opposite. There's just like me rambling for like two minutes, pressing down the function key and then let go and then like hopefully it figures it out. And oftentimes it does. Yeah. But it's not as thoughtful as like a structured prompt with like, you know, well run communication as though it's a PRD or a memo. Is that in line with how people do? There's like basically bimodal prompting where there's some problems where you spend a lot of time up front and other problems you just dash it off.
I don't think the voice is necessarily low. I think it's like more like how much information is in the prompt. The model can like you can um and uh and like add some sentences and be like, oh, like actually I changed my mind like in the middle of the prompt and it will be able to follow that perfectly. You know what I mean? So I think the like actual format of the text is less important, but then like the ability to like how much information is in it, right? And I think for voice, a lot of times, you know, going back to like kind of human-agent interaction and like for a lot of people, it's just way easier to talk than to like type, you know? And if that gets more information out of you, like that's better.
At some level, it feels like just giving the model as much context. Yes. Over-prompting before you kick off is a best practice. I don't know. A lot of the times, like when I was first trying out Fable, I spend a solid 30 minutes like really crafting a long problem. This, I think, is a response of models running for longer and longer, right? It's still a little difficult to nudge them as they're in like, you know, in the loop, but I just like intuitively spend more time kicking off that first prompt and working with it a lot.
My personal opinion is that if I was a software engineer, if I was like, you know, just running my own startup, for example, I think I would mostly stick to a max 20X, you know what I mean? Like maybe verification and code review are kind of like separate things. But I think like what I see a lot of times is people hit rate limits when they're doing this sort of like, oh, like it did a lot of work and you're like, oh, I don't like this. Like, can you like undo this and redo it? And then you're like iterating on this like thing that the model could have done if you had like spent more upfront time or given it better context, you know? And instead, it's sort of like, you're like, nope, don't like that design. Try this or like, you messed this up or something like that. And then that just eats up so much more of like, you know, your usage. And so that's like, I think maybe like a key like tip both for like efficiency as well. Right. And yeah, I think like context and not just like context on like what the goal is, you know, I mean, it's good, right? Like, are you building a prototype or is it like a production thing? Like, where can you spend compute or where can you not spend compute? Like, I think you have to give the model permission or like not permission to do things sometimes where, you know, like it doesn't know intuitively how much you want to spend on this task, right? And you can, you can use effort for this. So I'm working on a blog post about that where it's like, you know, if you want for like, we see that effort scales with basically the complexity of the task. So for security, effort gets like way more results. Like high effort versus like low effort gets like changes the evals a lot. But for software engineering, it doesn't change it a huge amount because effort is mostly spent on the verification and the like edge case testing and things like that. And so like being able to like give the model that guidance of like, hey, this problem is something that I think I want you to spend a lot of time verifying and edge case testing. How about
model in the mix? So, you know, there's opus and fable with effort. There's also haiku in there. Yeah.