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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. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
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 for AI and automation?

The randomness knob on an LLM's output distribution. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.

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