Mind Readings: Designing AI Efforts for Scale

In today's episode, let's talk about designing AI efforts for scale. Are you designing yours for scale? I've mentioned for years now in Keynotes that generative AI prompts are essentially their software, right? They're code. When you're using Chat GPT or Google Gemini or EnthapCloud, you are writing code.

You just happen to be writing code in English or Danish or Ukrainian instead of Python or Java or C. And most people aren't coders, which is why most people don't get the kind of results out of generative AI that they think they should be able to, because they're not writing code properly. One of the lessons I learned early on in software development is that you design, you design software to scale. You design software to be reusable, you design software to be modular and as efficient as possible. You don't write a piece of software just to solve the problem you have in front of you today.

You write a piece of software so that's repeatable and scalable so it solves today's problem, tomorrow's problem, and solves the problem for more people than just you if you want your software to be good, right? A simple example is like variable assignment. In regular software like R or Python, you might want to process a file. And the the single use non-scaled mindset is you know, file equals the location on your computer. I mean, that works.

The software will know to go to that place and get the file, but that software will only work for you, right? It will not work if you get a new computer and your hard drive name is different. It will not work for your friend, it will not work for your colleague. It may not even work for you two years later, right? Because it's it's hard-coded.

It is so hyper-specific. If you wanted it to scale, you'd want to have an input from the user and say, hey, user, where is the file? It's specified by a command line argument or prompt to the user to say, hey, you know, where is this thing? And the same general concept holds true in generative AI prompts. You can write a prompt just for the challenge you want to solve today.

Write me a blog post about this, whatever. Or you can write a prompt that you can reuse over and over again or turn it into software like a custom GPT. I I know AI OpenAI calls them just GPTs now, but that is maddeningly generic. Um if you build a prompt that is so thorough and so real computer code, you can scale it. For example, you can convert any prompt into what's called a priming representation, basically a really detailed outline that's instructions for a language model that takes advantage of the model's knowledge, uh significantly increases the model's precision.

Uh you can incorporate variable names, chat GPT in particular supports us with brace, you know, curly brace holders, I think. Write if this then that branching logic, all sorts of code structures you can write in generative AI prompts. You don't have to write them in like Python syntax, you can just write them in plain language, but the structure of an if-then loop is still the same. What this means is that if we want to continue unlocking value from generative AI, we have to graduate from the simple, you know, write me a prompt, do this just right now, to how do we solve this problem at scale? How do we build tooling to solve this problem at scale?

How do we adopt a programmer's mindset to the language we use with these tools to get better, more scalable, more reliable results? As my uh my partner friend Katie Robert talks about all the time, it's about getting people to work together, people and processes in place to make the technology work. Because generative AI is just another technology. And as Katie often says, you know, new technology doesn't solve old problems. If you've got people who are writing terrible code, whether they write it in Python or whether they write in English, it's still terrible code.

You haven't solved the problem. But if we get people to work together, if we get coders to teach non coders how to think in code, even if we're writing in English, we will get much better results. We will scale our AI AI efforts, we will make our AI efforts more robust, we will get better results, we will be able to turn them into applications that deliver value above and beyond just solving the problem that's in front of us today. So that's going to do it for today's episode. Thanks for tuning in.

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