title: "Alright, Let's Talk About Writing Prompts... Again. But This Time, Maybe It's Different?" date: "2024-07-28" excerpt: "Feeling stuck writing AI prompts? Another tool landed on my desk, promising 'Google engineer best practices.' My first thought? 'Yeah, right.' But diving in, maybe there's something genuinely useful here. Let's poke at it."
Alright, Let's Talk About Writing Prompts... Again. But This Time, Maybe It's Different?
Honestly, if I see another "master prompt engineering" guide or "ultimate prompt generator" list, I think I might scream. It feels like everyone's got the secret sauce for talking to AI, and mostly it just adds to the noise. You spend more time trying to figure out how to write better AI prompts than actually getting the AI to do something useful.
So when I stumbled onto something claiming it could help generate high-quality, efficient prompts by leaning on "Google engineer best practices," my built-in BS detector went off pretty hard. Google engineers? The folks who built some of this stuff? Okay, maybe that's worth a second look, even if it just tells me something I didn't know before. The page I ended up on was over at textimagecraft.com/zh/google/prompt – yeah, it's got the Google angle baked right into the URL.
My immediate question, and probably yours too, is: "Okay, what does 'Google engineer best practices' even mean in the context of prompting?" Are they talking about being super explicit? Providing negative constraints? Breaking down complex tasks? Giving examples? It's a bit vague, which is initially annoying, but also... intriguing? It suggests there might be a more structured, perhaps even scientifically tested, approach to writing prompts for AI models than the usual trial-and-error dance most of us do.
Think about it. If you're building the large language models themselves, you probably develop some pretty deep insights into what kind of input makes them sing versus what makes them hallucinate or give generic answers. There's gotta be a method beyond just throwing words at the wall. This tool, then, ideally takes that implicit knowledge – the kind you get from building these things – and distills it into a process that helps you improve your prompt engineering.
What I'm hoping for, what makes something like this potentially different from the million other prompt helpers out there, isn't just a list of pre-written prompts (though examples are always nice). It's a framework. It's a way to think about structuring your request that leads to better, more predictable results. Maybe it guides you through adding context you didn't think was necessary, or helps you define the desired output format more precisely, or prompts you to specify the AI's role. These are the sorts of things that can really make a difference between a prompt that gets you a decent first draft and one that gets you exactly what you need, saving tons of back-and-forth editing.
Trying to figure out how to write better AI prompts is basically trying to speak the AI's hidden language. And perhaps people who built the AI have a better Rosetta Stone than the rest of us have found yet. That's the promise here, isn't it? To tap into that deeper understanding to generate high-quality prompts quickly and efficiently. It's less about giving you the answer, and more about teaching you how to ask the question in a way the AI understands best.
Does it live up to the hype? Like with any tool, especially in this space, it's going to depend on your specific needs and the models you're using. But the angle of basing it on insights from the builders themselves? That's a hook that actually grabbed my attention in a crowded market. It suggests a potential path to generating effective prompts that goes beyond the surface level. If it helps me stop guessing and start prompting with a bit more intention, that's a win in my book. Anything that streamlines the process of using AI for creation or analysis, anything that makes writing prompts for AI less of a chore and more of a skilled craft, is worth exploring. Because ultimately, the output is only as good as the input. And if someone's offering a peek behind the curtain at what the experts consider good input... well, I'm listening.