logit appears in this channel's captured material in the context of how a model turns internal numbers into word choices. Before a language model produces its next word, it computes a raw score — a logit — for every token in its vocabulary; only after those scores are transformed (typically via softmax) do they become the familiar probabilities behind sampling. In this channel's material, logit functions as that raw, pre-probability score: the intermediate value you would point to when explaining that a model's output is not magic but a ranked set of numbers that get normalized into probabilities. It belongs to the same family of mechanical, inside-the-model vocabulary as token, probability, and sampling, and it is useful for a reader who wants to understand what is actually happening under the hood when a model generates text — rather than treating the output as an opaque oracle.