Chapter C · 8 pages · ~115 min
What is a large language model?
Build a practical mental model of how large language models learn patterns, generate text one token at a time, gain useful capabilities, and sometimes produce convincing mistakes.
Last reviewed 2026-08-08.
What you will be able to do
- Explain next-token prediction without implying that a model literally thinks or retrieves exact training records
- Trace how pretraining turns examples into reusable statistical patterns stored in model weights
- Describe generation using tokens, probability distributions, temperature, and context
- Recognize useful capabilities, in-context learning, hallucinations, and important operating limits
- Select and use a model by balancing quality, speed, cost, privacy, context, and evaluation evidence