What Is Temperature? Controlling Randomness in Output
DICTIONARY · AI

What Is Temperature in AI?

Temperature is a setting controlling how much randomness a model uses when choosing each next token.

In plain English

At a low temperature the model consistently picks the most likely continuation, producing predictable, repeatable output. At a higher one it sometimes picks less likely options, producing more variety.

It is often described as a creativity dial, which is misleading. It controls variance, not quality. High temperature produces different text, not better text.

What to know

Low
Predictable and consistent. Right for extraction, classification and structured output.
High
More varied. Useful when generating options to choose between.
Not quality
Randomness does not improve reasoning or accuracy.
Task-dependent
There is no correct default across different jobs.

Why it matters

For any workflow producing data rather than prose, low temperature is almost always right, because you want the same input to produce the same output. Leaving it high in an extraction pipeline is a common and confusing source of inconsistency.

Common mistakes

×Raising temperature to make output more interesting when the prompt was the problem.
×Using a high setting for data extraction or classification.
×Expecting reproducible results at high temperature.
×Adjusting it before improving the prompt.

FAQs

What temperature should I use?

Low for anything structured or factual. Higher only when generating variations to pick from.

Does low temperature make output boring?

It makes it consistent. Interest comes from the prompt and the material.

WRITTEN BY TARIQ SALLAM
Marketing Consultant. Entrepreneur. Content Creator.

I'm a marketing consultant, entrepreneur and content creator. I help businesses grow through practical marketing, websites, SEO, content and AI.

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