Sampling Temperature
Definition
Temperature affects the randomness in the selection of the next parts of the text.
Temperature is a parameter of the probability distribution for token selection in supporting language models. Lower values tend to lead to less variation, higher ones to more variation. Neither factual fidelity nor identical results are guaranteed.
Simply explained
Like a regulator between more similar and more varying formulations.
Why it matters in practice
Helps to realistically classify the possibilities, quality and limitations of AI systems.
Example from business
Possible example: A team tests lower values for structured outputs and compares the results.
Limits and pitfalls
- Equate a low value with fact-checking or completely identical answers.
Origin & history
API documentation describes temperature as a parameter of output selection. Available values and support depend on the model; the parameter does not guarantee factually correct answers.
Use in mid-sized companies
- Possible application in SMEs: A team tests lower values for structured expenditure and compares the results.
Frequently asked questions
What does sampling temperature mean?
Temperature affects the randomness in the selection of the next parts of the text.
What errors should be avoided?
Equate a low value with fact-checking or completely identical answers.
Sources
- Prompt engineering: Temperature and sampling · Microsoft, Prompt engineering: Temperature and sampling
Sources checked on 09.10.2026. Editorial application examples are hypothetical; no proof of real customer results.
Editorially reviewed on: