What is temperature in AI?
Updated · By Robert Breen
Temperature is a setting that controls how much randomness an AI language model uses when choosing its next words. A lower temperature makes answers more predictable and consistent; a higher one makes them more varied and creative, with a greater chance of drifting off course.
Why it matters for a small business
Different business jobs want different amounts of variety. Sorting a request into Urgent, This week or Backlog should give the same answer every time the same request comes in. Brainstorming ten headline ideas benefits from variety. Temperature is the dial between those two behaviors, and most tools pick a sensible middle value for you.
It is worth knowing about mainly so you reach for the right fix. If an agent gives inconsistent labels, the first fixes are clearer rules and a fixed list of allowed answers; lowering temperature can help after that. If brainstorm output feels repetitive, raising it a little can help. It will not fix missing facts, and some newer reasoning models do not support changing it at all.
In a real lesson: Build an AI Maintenance and IT Request Form
The lessons leave temperature at its default, so treat this as where you would find it rather than a step you click. In the maintenance and IT request form lesson, Ridgeline Supply Co., a made-up warehouse business, sends every form submission to an n8n AI Agent with gpt-5-mini as its model.
The system message asks for a judgment with fixed answers: set a priority of Urgent (safety risk, or it stops shipping or receiving), This week or Backlog, and assign one team, Facilities, IT or Equipment. When you test it, a stuck dock door with trucks waiting comes back as urgent for facilities. That is a job where consistency matters more than creativity.
Notice what keeps it consistent: the system message defines each priority and lists the only allowed teams. If you later saw the same kind of request labeled differently on different days, tightening those definitions would come first. The OpenAI Chat Model node's options include a sampling temperature setting, which you could lower afterwards if the model you chose supports it.
Try this lesson free or read the step-by-step guide.
Common confusions
Temperature vs model quality
Temperature does not make a model smarter or better informed. It only changes how adventurous its word choices are. A wrong answer at high temperature is usually still wrong at low temperature if the facts are missing.
Temperature zero means fully repeatable?
Not guaranteed. A very low temperature makes outputs much more consistent, but small differences can still appear between runs. Rely on clear rules and allowed values, not temperature alone, for anything that must be exact.
Tips
- Leave temperature at the default until you have a specific consistency or variety problem.
- For classification and extraction, fix the allowed answers in the prompt first.
- Change one setting at a time and test with the same few inputs so you can see the effect.
Related terms
Where you use it: free lessons
- Build an AI Maintenance and IT Request Form (Lovable and n8n, 15 min)
Frequently asked questions
- What temperature should I use for business emails?
- The default is usually fine. If replies vary too much in structure, tighten the format instructions first; lower the temperature only if that is not enough.
- Can I change temperature in ChatGPT?
- The ChatGPT app does not offer a temperature control. It is available when you call models through the API, for example in n8n's chat model nodes, for models that support it.