Definition
Temperature scales the probability distribution before sampling. Low values (near 0) make outputs more deterministic; higher values increase diversity and surprise.
It does not make the model "smarter" — it changes how boldly it samples from what it already believes.
In simple terms
Low temperature is carefully picking the safest menu item. High temperature is trying the chef's weird specials — more variety, more risk.
Where you see it
- Customer support bots use low temperature for consistent answers.
- Creative writing uses higher temperature for varied prose.
How it works
1.Model scores tokens
Logits for each vocabulary item.
2.Divide by temperature
Higher T flattens the distribution.
3.Sample
Draw the next token from the adjusted probabilities.
Why it matters
- Temperature is one of the first knobs product teams tune for reliability vs creativity.
Often confused
Temperature 0 guarantees factual correctness.
It reduces randomness but the model can still confidently produce false content.