How to use AI: Prompts vs. Projects vs. Agents
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Should you just ask ChatGPT a question, write a structured prompt, build a project, or maybe go deploy an agent? We're going to help you figure out which one you need today. Welcome, humans, to this week's Neuron Podcast. I'm Corey Knowles, and as always, joined by Grant Harvey. Hey, Grant. What's up? So over the past two and a half years, using AI has gotten a little more complicated. You have more options than ever, and they're actually quite different from one to the next. And we're not just talking about companies. We're talking about models and ways to approach models, and each have their strengths and weaknesses. So, regardless of which company's tools you prefer, today we're going to explain how to pick between an agent, a project, a structured prompt, or just typing a bunch of words at it and hitting enter and moving on, because each one of those kind of has its place, so...
To kick us off, Grant, how would you define prompt?
Well, the prompt is what you input into the AI chat window. Or if you're working with an API, it's what you send over the wire into the cloud and what is then used to give you an output. Okay.
Yeah. It's a question. Then let's talk about how we would describe, say, a simple prompt.
Okay.
I think it's good to have these definitions clear up front. So as we go forward, it all makes sense to anyone who's listening.
So the most basic possible prompt you could do in AI is basically asking it a question, like a single question with a single answer. Fix this. Well, yeah. Yeah, exactly. But even simpler than that, right? Using it like a Google search, like saying, please explain to me the fall of the Roman Empire. You know, you're not going to get a simple answer, but, you know, that's a simple question, right? Yeah. Yeah. Now, what you just described is basically giving it context along with your prompt. So in the example that you said, let's say you have... something you wrote, like your bad essay about the fall of the Roman Empire, and you say, fix this, and you paste the whole essay that you already wrote into it.
Now, you're leaving a lot up to the AI to decide what fix this means. And they're pretty smart, but they're not mind readers, okay? They're probabilistic predictors. So they're going to try and fix it based on what's in their training data and what they've seen is like a good essay on the fall of the Roman Empire. Um, but let's say you were to take a screenshot of like an error, uh, in some code that you're writing and you say, fix this. Well, it's going to be able to use reasoning depending on what model you use, which we'll get into that, uh, to think about the error and say, okay, here's what I need to fix this error. Now it probably needs to know your code so that it can actually fix the error in the screenshot.
So you might also need to paste in the code, or if you're working in a coding app like cursor, which we. I can get into that, but that's not really the subject of this episode. It will already have that context, but that's essentially how it works. You give it a bunch of data, essentially, and a very specific goal at the top, and it then goes to work crunching the numbers to try and figure out, okay, let me try and solve this problem for this person.
And for most of your day-to-day tasks, it's probably enough.
It's true.
you know, the truth is the vast majority of like, You know, most of my interactions with AI are on, you know, simple needs. Like, can you find me an article on this? Can you, you know, sort this out? Copy edit this document for me, whatever the case is, you know. And that kind of stuff, it doesn't have to be anything fancy for it. It can be super simple. But that brings us to our next question, which is structured prompts. Which is a very different way of approaching, you know, when you get into, you know, and you'll think of this more with research models, like when 01 and 03 dropped, when Claude starts dropping research tools at Gemini, one of the things you start finding is like, here are these prompt patterns that are really useful and they're very...
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