Temperature
A sampling parameter applied to the next-token probability distribution at generation time. A low value sharpens the distribution toward the single most likely token. A high value flattens it and lets less likely tokens win more often. Changing it changes how the model samples, not what it knows.
Why exams ask this
The trap answer treats temperature as a creativity switch or an accuracy switch. It is neither: a model at temperature zero can still produce a confidently wrong answer, because temperature reshapes sampling over a distribution the model already computed. It never touches what the model actually knows.
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AI Fluency: Framework and FoundationsGenerative AI FundamentalsGenerative AI LeaderOCI Generative AI ProfessionalAI Agents CourseGenerative AI and LLMs, NCA-GENLAI Engineering Professional CertificateCertified Machine Learning Engineer, AssociateClaude Code in ActionClaude Certified Architect, Foundations
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