Somewhere around when LLMs started eating every roadmap, a quiet belief took over a lot of teams. If the model is big enough, it'll figure out the patterns on its own. Why bother hand crafting features when you can just throw raw data at something with a few hundred billion parameters.
That belief is wrong for most of what people are actually shipping. I've seen it cost real accuracy in production, not theory.
Here's the thing nobody likes admitting out loud. The algorithm you pick matters far less than the features you feed it. A plain logistic regression with smart features will often beat a fancy neural network choking on raw, messy data.
Let's take churn prediction in e-commerce, since most of us can picture it immediately.
The lazy version
