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This article breaks down the core concepts behind LLMs—from next-token prediction training to tokens, vectors and attention layers—to show how they generate text. It also covers context windows, parameters and why model scale affects performance.
- LLMs work purely through next-token prediction learned by hiding and guessing words billions of times during training, not through actual understanding of letters or math.
- Context windows now range wildly, from 200K tokens (~150K words) up to Llama 4 Scout's 10 million tokens, but bigger windows don't fix factual errors or logical gaps.
- Because models process text as tokens/vectors rather than raw letters, they inherently struggle with tasks like counting letters or doing arithmetic.