Temperature
Temperature is a parameter that determines how much randomness there is in a language model's predictions when generating text. A higher value produces more creative and varied responses, while a lower value produces more conservative and predictable responses.
On this page
Source content checked . English translation published 2026-10-27.
In business automation
When an AI agent drafts a response to a customer or fills in fields in a system, it is useful to know how predictable its responses will be. Temperature is a parameter that determines how much randomness there is in the model's predictions when generating text. According to Anthropic, a higher value leads to more creative and varied responses, while a lower value leads to more conservative and predictable responses.
Examples
These examples illustrate a possible workflow. Adapt the connections and rules to your own systems.
Data extraction versus idea generation
A team uses the same model for two tasks: extracting order details from an email and writing ideas for a post. For extraction, they choose a low temperature to get consistent responses; for idea generation, they choose a higher temperature to get varied wording.
Relevant tools
- Anthropic
- This definition is from Anthropic's glossary.
What to check
- Assuming that a low temperature guarantees a correct answer. It affects the degree of randomness, not factual accuracy.