Temperature is an LLM inference parameter. At temperature 0, the model picks the highest-probability next token every time — output is deterministic and (mostly) reproducible. At higher temperatures, the model samples from a wider distribution — output is more varied and creative. Practical guidance: use 0-0.3 for factual Q&A, code generation, classification (you want consistency); use 0.6-0.8 for creative writing, brainstorming, summarization with style; rarely go above 1.0. Combined with `top_p` (nucleus sampling) for finer control. Temperature is the single most under-tuned parameter — many teams ship at the default (often 1.0) when 0.3 would give better results for their use case.
מילון
מה זה Temperature?
A parameter that controls how random an LLM's output is — 0 = deterministic and safe, 1 = creative and varied, >1 = chaotic.
מונחים קשורים
LLM (Large Language Model)
An AI system trained on massive text datasets to predict and generate human-like text — the technology behind ChatGPT, Claude, Gemini, and most modern AI chatbots.
Inference
The act of running a trained AI model to generate output — i.e., 'using' the model, as opposed to training it.
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