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In the context of AI and machine learning, temperature refers to a parameter that controls the randomness in the generation of predictions, outputs, or choices by a model. A higher temperature increases diversity and randomness in the results, while a lower temperature makes the model’s outputs more deterministic and confident, often used in models like language generators to balance creativity and coherence.