AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
Temperature is a setting that controls how varied or predictable an AI model's output is, from strict and repeatable to loose and creative. Low temperature suits tasks that need consistency, like data extraction. Higher temperature suits brainstorming. The wrong setting is a common cause of unreliable output.
Temperature is the dial between a strict accountant and a creative director. For the accountant you want the same answer every time. For the creative director you want variety and surprise.
A team discovers their contract data extraction tool occasionally produces different field values for the same document. Lowering the temperature from 0.7 to 0.1 makes extraction deterministic and auditable.
Set temperature near zero for any task requiring consistent, repeatable output: data extraction, classification, structured responses. Set it higher for creative tasks where variety is welcome.
High temperature on analytical or factual tasks introduces noise that looks like intelligence. Varied answers in those contexts are a reliability problem, not a feature.
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