The likelihood a model assigns to each candidate next token; low probabilities flag where the model is uncertain about your brand or topic.
Token probability is the likelihood a model assigns to each possible next token as it generates text. Every word it outputs is a pick from a probability distribution over its whole vocabulary, and those probabilities reveal how confident — or uncertain — the model is at each step.
This is the raw signal behind our Tree Walker: word rarity comes from how common a token is, and word uncertainty comes from how flat the probability distribution was when the model chose. Where probabilities are low and spread out, the model hesitated — a weak spot in how it describes your brand.
These probabilities are also what sampling controls like temperature and top-p sampling reshape, and they trace back to the tokenization scores baked in during training. Reading them turns AI perception into a measurable target.
