Whether you're an AI advocate or skeptic, there's no doubt that it is here to stay, and it is already having a massive impact on our personal and professional lives. The speed at which AI tools and models are being developed is staggering, in part a result of AI companies reaching the point where their own AI tools write the code for ongoing evolution and refinement of the AI tools! Development is no longer restrained by the speed of a human. This is simultaneously impressive and concerning. There's a near certainty that by the time this article goes to print, there will already be some major changes in AI. However, the aim of this article is to talk about the fundamentals of working with AI in order to better understand the core concepts, which, and I hesitate to write this, are unlikely to change significantly.

I'll describe the steps for building an AI-generated applications – first from a more intuitive approach that some might think would fall into the category of what is considered vibe coding. Along the way, I'll point out some of the problems that come with adopting this method. Then I'll show you a more rigorous approach that will produce a result that is perhaps closer to professional software development. But first, I'll begin with some key concepts.

Tokens

Tokens are the currency of LLMs. They're used to measure usage, limits, and pricing. An LLM doesn't see text the way you do, so any text you send it is first broken down into tokens. For example, "cat" might be one token, and "cats" might also be one token. Here is another example: "Unbelievably I can't stop." is four words but seven tokens. The two culprits are:

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