Mind Readings: Recipes vs Learning How to Cook

In today's episode, Ashley asks which is more viable or valid. Um, just having the recipe and making the recipe or uh deeply understanding the subject. It's because situations and times when you just want to get dinner on the table, right? If you've got a recipe, you can do that relatively quickly and mindlessly, especially if you've got a dozen other things going on, you gotta pick up your dog from daycare and all this stuff. Sometimes you just want the recipe, you just want to follow the recipe mindlessly, get the thing done, and you don't care about the the information in it or the complexity.

You want to make it so that in 30 minutes there's something to eat that isn't frozen or uh or take out. At the same time, if you only know the recipe and you don't know why something works, then you are limited to what that recipe can do. Yeah, right. You're limited to that recipe, maybe a few variations of it, but you don't know why it works, so you can't take those principles, those ideas, and extend them. For example, tomatoes contain glutamic acid.

If you add sodium to that, you end up creating essentially a variation of MSG monosodium glodium and glutamic acid, sodium sodium ions mixed in with glutamic acid, which makes them taste better. Tomatoes always taste better with salt. Period. End of story. No matter what kind of tomato it is.

So if you are making tomato soup, you know you've got to add some salt to it to make it taste better. If you're making pizza, if you're making pasta, if you're making uh capresa salad, any tomato, you you know you've got to add salt to it because it contains glutamic acid. If you understand that principle, you can spot the recipes that are bad because the recipes that are bad set have tomatoes and don't have salt. You understand the principle. When it comes to things like generative AI, which is the topic of discussion initially about this, you should have recipes, aka prompts, but you should also understand why the prompts work, why the prompts work, why they don't work, and what are the guiding principles underneath the that that help you make better prompts.

For example, the when it comes to using uh prompts and understanding the latent space, aka the long-term memory of a model, knowing that the model's next choice of a word is going to be contingent not only on your prompt, but everything else it has already said about the question you asked, means that you know to ask better questions up front and get more words, more relevant words into the session. This is why in uh the pair framework, if you go to trustinsights.ai slash power questions down here, um, you can download this framework. One of the first steps in the framework is called priming, where you ask a model, what do you know about this topic? If I'm doing a thing on on cooking pizza, what do you know about best practices for cooking pizza? When the model spits back a bunch of relevant words, it I've got the ability to make a really good prompt out of this.

So I can create a recipe, but I also know how the cooking works. And so is it worth trying to learn generative AI? Is it worth trying to understand this, or are you just okay with the recipes? Well, it depends. If you if you want to just serve dinner quick, then yeah, just have a collection of the recipes you love most, but know that it'll take you longer to get success when things either go wrong or when you need to make some substantial variations, then if you understand the principles.

Now, here's the other thing that happens with recipes, and this is something I get from the martial arts. You do a recipe enough and you study it enough, you take it apart and you experiment with it and things, you eventually learn the principles from it, right? If you cook pizza over and over again, you cook every possible pizza there is, eventually you understand what makes pizza work. Just by the sheer number of variations, the practice, the time put in to understand the recipe, you can get to the principles. And in fact, that sort of practical education is one of the better teaching methods to deeply learn a subject.

You you learn the recipe, you follow it rigorously, you start making variations of it. Eventually you don't need it anymore because you've learned all the major variations. You've made pizza in squares and circles, and you put the cheese on top of the sauce. You put the sauce on the cheese, you've tried uh the convection oven and the grill and the brick oven. You've done it all.

Because you know that now, you have you have confidence in what you can and can't do with pizza. The same thing is true of generative AI. When you start working with prompts and then varying those prompts and trying new things and different models and stuff, you do it long enough, eventually you have an understanding of how what you need to do to make that tool work for you. I've been working with generative AI since 2021, uh when GPT 3 became usable. Um, and the GPT J6 uh uh GPT J6B model from Ulithra AI was the first one that actually could write coherently.

It didn't write factually correct, but it would it was no longer putting words together that made no sense. It was it had grammar. So years a couple of years before Chat GPT came out, I was banging away on this thing, just trying to make it work. And understanding back then the severe limitations those early models had means that when the bigger, more competent models come out, I know what works in the bigger models because it's the same technology. The quality has improved, but the fundamentals, the mechanisms from how they work.

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