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AI & Machine Learning · Foundational

Temperature

An inference parameter that scales randomness in token selection; low values are more deterministic, high values more creative.

Definition

Temperature divides the logits before softmax. Near 0 the model almost always picks the most likely token; higher values flatten the distribution, increasing variety and the risk of errors. Typical ranges are 0 to 1 (some APIs allow up to 2).

Example

Use temperature 0-0.2 for data extraction and 0.7-1.0 for brainstorming.

Exam tip

Lower temperature for factual, consistent answers; raise it for creative variety.

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