The prompt defines what the model should do, while temperature changes how varied its token choices are. Clear instructions can reduce variation, but they cannot make high-temperature sampling fully predictable.
Use a low temperature for extraction, classification, strict formatting, and other tasks with one preferred answer. A moderate value may help when you want several ideas, tones, or creative drafts. Temperature does not correct a vague prompt or make unsupported facts true.
Some current models ignore or restrict temperature, especially when using reasoning modes. Check the model's API. Tune settings on repeated evaluation runs, and change one sampling control at a time so you know what caused the difference.
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