Give me 3 alternative titles for this article. Constraints: each <= 8 words, no clickbait, different angle each. Article summary: [paste] Return a numbered list only.
Opens the analyzer with the prompt prefilled.
Open in PrompteaTemperature
A sampling setting that controls how random a model's word choices are: lower values are steadier, higher values more varied.
Last updated: · Markdown version
How to apply it in prompts
Use this concept to reduce ambiguity and improve consistency. If you need strict formatting, combine it with a JSON guide and a per-model template.
What it means
- Definition: at each step a model assigns a probability to every possible next token. Temperature reshapes those probabilities — low values concentrate on the most likely tokens, high values spread the choice out.
- Why it matters: low temperature suits extraction, classification and code, where you want the same answer each time; higher values suit brainstorming and creative drafts, where variety is the point.
- How to apply it: change it in the API or playground when the product exposes it. Some providers' reasoning models fix or ignore it, so check the current documentation of the model you use.
- If you can't control temperature, you can still push consistency with a strict output format, delimiters and one or two examples. Low temperature does not make a model correct, only more repeatable.
Copy-paste example
FAQ
What temperature should I use?
Low for tasks with one right answer (extraction, classification, code), higher for open-ended writing and idea generation. Start from the provider's default and move in small steps.
Does temperature 0 guarantee identical answers?
No. It makes outputs much more repeatable, but providers do not promise bit-for-bit identical results, so validate the output instead of relying on it.