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Dictionary

Temperature

The randomness knob on an LLM's output distribution.

1 min readupdated 2026-07-04

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Temperature scales the logits before sampling. 0 makes the model near-deterministic (best for extraction, code, JSON); 0.7-1.0 makes it creative (best for brainstorming, copy). The randomness knob on an LLM's output distribution.

The randomness knob on an LLM's output distribution. Temperature scales the logits before sampling. 0 makes the model near-deterministic (best for extraction, code, JSON); 0.7-1.0 makes it creative (best for brainstorming, copy). In practice: A JSON extractor runs at temperature 0.1; a hook generator runs at 0.9.
Definition
Temperature scales the logits before sampling. 0 makes the model near-deterministic (best for extraction, code, JSON); 0.7-1.0 makes it creative (best for brainstorming, copy).
Example
A JSON extractor runs at temperature 0.1; a hook generator runs at 0.9.
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What is Temperature?

Temperature scales the logits before sampling. 0 makes the model near-deterministic (best for extraction, code, JSON); 0.7-1.0 makes it creative (best for brainstorming, copy).

What is an example of Temperature?

A JSON extractor runs at temperature 0.1; a hook generator runs at 0.9.

Why does Temperature matter?

The randomness knob on an LLM's output distribution.

What should I learn after Temperature?

Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.

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