AI Fundamentals
Before you can build with AI, you need an accurate mental model of what it is. This section strips away the hype and gives you the vocabulary and concepts that the rest of the guide depends on — explained the way a software engineer would want to hear them.
In this section
Section titled “In this section” What Is AI? A working definition, narrow vs. general AI, and how the field got to foundation models.
AI vs ML vs DL vs GenAI The nested taxonomy every engineer confuses — clarified with a single diagram and table.
How Models Learn Data, parameters, loss, and gradient descent — the training loop behind every model.
What you’ll be able to do
Section titled “What you’ll be able to do”After this section you’ll be able to place any AI tool or paper into the right category, explain in plain terms why a model “learns,” and reason about why models fail — overfitting, bad data, distribution shift — instead of treating them as magic.
Prerequisites
Section titled “Prerequisites”None. If you can read pseudocode and you’ve called an API, you’re ready.