Model Temperature
The AI control setting that determines how predictable vs. creative the model’s answers will be.
Plain Language Definition
Model temperature is a setting that controls whether an AI gives highly creative, varied answers or strict, factual, repetitive ones. Low temperature means the model picks the most probable words predictably. High temperature means it takes more risks, producing more creative but less reliable output. Most AI search systems use low temperature for factual queries to ensure accuracy.
Technical Definition
Hyperparameter scaling logit output distributions before the Softmax normalization step in next-token prediction — lower values sharpen the distribution toward high-probability tokens (deterministic, factual), higher values flatten it (creative, variable).
Why This Matters for AI Search Visibility
Understanding temperature explains why AI answer engines that answer factual search queries sound consistent and convergent — they are set to low temperature to prioritize accuracy. For factual content to be cited correctly, it must be accurate and clearly stated, since there is no creative reinterpretation.