Once you understand how large language models actually work, you can reason about why a model is behaving a certain way instead of randomly guessing and pasting in a bunch of prompts. And to be clear, that doesn't mean the math. You don't need the internals of the attention mechanism or the ability to derive formulas.
You need a practical mental model: tokens and why models think in tokens instead of words, what context is, what a context window is and why you can't just dump 10 billion tokens into a model and expect it to work, and message roles like system, user, and assistant, since those are what you'll be using through an API.
That's the difference between someone who knows how to use AI and someone who can engineer with it.
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