Temperature

Webisoft AI team Updated 2 min read

Temperature is a setting that controls how much randomness an AI language model applies when picking each next token: at low temperature the model almost always chooses its most likely option, producing consistent, predictable output; at higher temperature it samples more freely from less likely options, producing more varied, and less controlled, output.

The scale typically runs from 0 upward (commonly 0 to 1, sometimes to 2). At or near 0, asking the same question twice yields nearly the same answer. At high settings, phrasing, structure, and even substance vary run to run. Temperature doesn’t make a model smarter or better informed, it only changes how adventurous its word choices are. Even at temperature 0, outputs aren’t guaranteed perfectly identical, and a low temperature does not prevent hallucination; a confident wrong answer can be the model’s most likely one.

Most chat products set temperature for you. It becomes your decision when your team builds on an AI API, where it’s a standard request parameter.

Why it matters at work

Matching temperature to the task is one of the easiest reliability wins in an AI workflow. Extraction, classification, form-filling, code generation, and anything downstream systems parse should run cold, you want the same input to give the same output, every time. Brainstorming headlines, drafting outreach variants, or generating name ideas benefits from heat, you want ten different options, not one option ten times. Teams that leave a creative-writing temperature on a data-extraction pipeline get flaky, inconsistent results and often blame the model.

A work example

A revenue-ops team runs its invoice-field extraction at temperature 0 so every run of the same invoice returns identical JSON, but bumps temperature up in a separate prompt that drafts five subject-line variants for the follow-up email.

  • Token, the unit whose selection temperature randomizes
  • Hallucination, a failure mode temperature influences but doesn’t eliminate

Get one practical AI-at-work idea in your inbox each week, subscribe to the AI Work+ newsletter.

FAQ

What temperature setting should I use? Low values, near zero, suit factual and repeatable tasks like extraction and classification; higher values suit brainstorming and creative variety. When in doubt for work tasks, start low.

Does temperature zero make a model accurate? It makes output nearly deterministic, meaning the same input gives mostly the same output, but not more truthful. A confident wrong answer stays wrong at temperature zero.