Tokens and the context window
When you send text to a large language model, it doesn't parse individual characters or complete words directly. Instead, it reads and writes in tokens—discrete chunks of characters drawn from a fixed vocabulary established during training. Tokenization is the translation step that chops raw text into this sequence of numerical identifiers before computation ever begins.
Inference and sampling
If you submit the exact same prompt to a language model twice, you will often receive two distinct answers. This variability is not a glitch—it is an intentional feature introduced during generation by a configurable sampling layer outside the neural network itself.
Reasoning models
A reasoning model is designed to generate an internal scratchpad of intermediate thoughts before returning a final answer. If you ask it to solve a complex accounting problem, rather than immediately predicting the final total, the model spends hundreds or thousands of tokens working through calculations, verifying intermediate values, catching its own mistakes, and only then producing the visible response.