Language Models

Temperature

A setting for how adventurous or predictable the AI's word choices are.

In everyday terms

Low temperature: it nearly always picks the most likely next word (steady, repetitive). High temperature: it takes more chances (creative, sometimes nonsense).

For professionals

Divides logits before softmax. T→0 approaches greedy decoding; T>1 flattens the distribution. Often combined with top-p / top-k sampling.

Think of it like…

A cautious storyteller versus a wild one, telling the same story.

You've already seen it

Hitting "regenerate" and getting a different answer each time.

Myth vs reality

Myth: Higher temperature makes the AI smarter.

Reality: It only makes it more random. Good for brainstorming, bad for facts.

Quick check

For extracting exact figures from an invoice, you'd want temperature…

Show answer

Low: Low temperature = consistent, predictable output.

Builds on

Large Language Model (LLM)

Related

Large Language Model (LLM) · Hallucination

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