What is actually happening when an AI model “thinks”? When it’s drafting a response to us, how does it know what to say?

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LLMs don't 'think' — they are token prediction machines rooted in statistical language modeling dating back to WWII-era cryptography. Tokens are the smallest units of language a model processes, enabling flexibility and efficiency in understanding and generating text. Temperature controls how often a model picks less statistically likely tokens: low temperature yields predictable, conservative outputs while high temperature produces more creative but error-prone responses. The right temperature depends on the use case. Training data composition also directly drives AI bias, since token probability distributions reflect whatever content the model was trained on.

AI Crash Course: Tokens, Prediction and Temperature