Mind Readings: Focus On Differences In AI Prompts

In today's episode of Mind Readings, let's talk about the results of a recent paper that came out about in context learning. So if you're not familiar, in context learning is fancy engineering speak for basically prompting and data. What when an AI model is trained, in context learning allows us to summon the training data that a model was given or provide it new data, right? So in context learning is prompting plus data. Now, what this paper was saying, uh, which is very, very interesting, is that in context learning, we often over engineer our prompts.

And the uh the idea behind the paper is instead of telling the model everything about a particular task or particular idea, tell it the things that are different. Tell it focus on the things that are different. So, an example of this would be let's say you were asking the AI model to come up with a recipe. Generally speaking, from a language perspective, you don't need to tell an AI model what pizza is, right? It knows what pizza is, it has a mathematical average of all of the ways that people have described pizza over the decades that it's been a thing.

So you don't need to say, this is what a pizza is, this is how a pizza works, this is what a pizza looks like, and so on and so forth. It knows all that stuff. That's cut that's part of its training data. What you should focus on in a prompting is what's different about your pizza. So if you use the generic word pizza and you really meant pepperoni pizza, uh, you would just say, let's make a pepperoni pizza.

You don't have to define a pizza. Now, say a pizza is round and it's made up dough and stuff like that, it knows all that. What you would say is let's make a pepperoni pizza. If you wanted to make a prepare pizza that was gluten-free, you would say, Let's make a gluten-free pizza. And again, you don't have to define what those terms mean.

So what this means is, especially as you start building more advanced prompts or things like GPTs or gems, you want to take the time to think about what is different. What does the model already know on a topic? And then how do you how do you prompt it in a way that takes advantage of its knowledge so you're not having prompts that are loaded up with unnecessary information, especially if you're trying to highlight differences. So what that means is if a model knows what a pizza is, and you keep saying the word pizza and uh and you spend a lot of time redefining what pizza means to it, you're actually reinforcing the generic definition. If you invoke it by saying, here's what we're doing today that is different, then you're putting more tokens, more language around what's different than you are what's the same.

So you instead of saying, you know, here's the pizza is here's the way a pizza is shaped, you'd say, we're making pepperoni pizza today. I want a spicy pepperoni, I want pepperoni to be like this, I want this style of pepperoni, uh, I want to lay out the pepperoni like this on the grid, uh, or what or however it is you prepare it. By reinforcing the language that is different, you will get a better results faster and with less data. Now, for your average user, conversationally, you don't need to worry too much about this. So, if you're just using Chat GPT to a Gemini or Claude or Quinn, and you are prompting it in conversations, this is not a huge concern.

Because in conversation, you can always, if it comes back and says, ah, you meant plain pizza, right? And you can always say, no, no, no, no, I'm in preparati pizza. And you can provide those corrections. Where it's really important to do this work in advance is when you start building things like gems or GPTs or Claude projects. And especially, especially, especially when you're building agents.

Because an agent is going to execute the same set of prompts over and over and over again. It's just like embedding uh gender of AI into code, like in a Python application. The moment you start embedding it into code, you are now starting to um you are starting to create the opportunity for multiplicative effects with your prompts. And by that I mean uh your prompts, if there's a mistake in them, there's an error in them, the prompts will uh by nature of the fact that it's inside of an agent, have multiplicative effects. So errors get compounded over time.

And so we want to we want to nip that in the butt as by taking the time to focus on what's different in our prompts and what the model already knows, so we don't have to repeat ourselves. So as you work with these tools, tell AI what is different that you want it to do, and you will likely get much greater success. That's this episode of MyMings. Talk to you on the next one. If you enjoyed this video, please hit the like button.

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Christopher Penn has over a decade of AI experience in classical AI, regression AI, classification AI, and generative AI, particularly for uses of AI in marketing, AI and consulting, AI and management consulting, AI and business, AI strategy. Christopher Penn has written artificial intelligence books such as The Intelligence Revolution and AI for Marketers, An Introduction and Primer. Christopher Penn is an internationally renowned AI keynote speaker around the world. Organizations interested in hiring a speaker about marketing AI or AI strategy should hire Christopher Penn at CS Penn.