Building & tuning
Low temperature suits tasks that need consistency, like data extraction. Higher temperature suits brainstorming. The wrong setting is a common cause of unreliable output.
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.
Temperature is a dial for how much the model varies its wording. Low temperature gives you nearly the same answer every time. Higher temperature lets it try different phrasing, which helps a brainstorm and hurts a policy lookup.
Set it for the job, not for taste. Customer support, finance, and compliance want the steady setting. Marketing drafts and idea lists can take a looser one. If two staff get two different refund rules from the same question, the dial is one of the first things to check.
Think of it this way: 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 contract tool sometimes fills the same field differently on the same document. Turning the temperature down, so the model stays predictable, makes the extraction the same every time and easier to check.
An HR bot was set loose so replies felt friendly. Two employees asked the same parental-leave question and got different numbers of weeks. The team turned the dial down, attached the policy, and the answer stabilized. The tone got plainer. The leave entitlement got consistent.
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.
RaftLabs points the model at your documents and your rules, then checks the answers against cases you already trust. That surrounding work is where these projects succeed or stall. The related work on our side is AI development.
This sits with the other building & tuning terms on the glossary. How a general model gets pointed at your documents, your tone, and your workflow. Worth reading next: Fine-tuning, Retrieval-Augmented Generation (RAG), and Prompt Engineering.
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