How AI Learns, Thinks, and Decides

You’ve seen the pipeline — tokens in, words out. But how does the model know anything in the first place? How did it get good at this? And why does it sometimes give different answers to the same question?

These four concepts explain everything.

The Four Things Worth Understanding

Training is how the model becomes capable. It reads text, predicts what comes next, gets told how wrong it was, and adjusts. Trillions of times. After enough repetitions, it stops being wrong.

Inference is what happens when you actually use it. The model runs your prompt through all its layers — once per token it generates — and produces output left to right. Every word you read was predicted one at a time.

Sampling is why the same prompt doesn’t always give the same answer. The model doesn’t just pick the most likely word every time — it samples from a probability distribution. Temperature controls how adventurous that sampling is.

Fine-tuning is how a general model becomes a specialist. The base model knows everything. Fine-tuning teaches it to behave a particular way — as a doctor, a coder, or a polite customer support agent.

Why Sampling Matters Most (For You)

If you use AI tools regularly, temperature is the one setting that changes your experience most:

• Low temperature → predictable, consistent, good for factual tasks
• Medium temperature → balanced, good for most writing and coding
• High temperature → creative, surprising, sometimes wrong

Most tools don’t expose this directly. But knowing it exists helps you understand why AI sometimes surprises you.

Next up: Before we dive deeper into how AI stores knowledge, let’s go one level down. How does the model even read your words? The answer is tokens — and it changes everything about how you think about AI input.