# Temperature

Canonical: https://aimastermindlab.com/glossary/temperature

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Temperature is a hyperparameter that controls the randomness of an LLM's output. A temperature of 0 produces deterministic, predictable responses. Higher values (0.7–1.0+) produce more creative, varied, and sometimes unpredictable text.

## What is Temperature in LLMs?

Temperature is a sampling parameter applied to the probability distribution over a model's vocabulary before selecting the next token. It controls the trade-off between **determinism** and **creativity**.

## How It Works

Before sampling, the model's output logits are divided by the temperature value:

- **Temperature → 0**: The distribution becomes highly peaked. The highest-probability token is almost always chosen. Outputs are deterministic and repetitive.
- **Temperature = 1**: The distribution is unchanged — tokens are sampled according to their raw probabilities.
- **Temperature > 1**: The distribution is flattened. Low-probability tokens become more likely, producing more varied and unpredictable outputs.

## Recommended Values by Use Case

| Use Case | Recommended Temperature |
|---|---|
| Code generation | 0.0 – 0.2 |
| Factual Q&A | 0.0 – 0.3 |
| Summarisation | 0.3 – 0.5 |
| Chatbot | 0.5 – 0.8 |
| Creative writing | 0.8 – 1.2 |
| Brainstorming | 1.0 – 1.5 |

## Top-p (Nucleus) Sampling

Often used alongside temperature. Top-p restricts sampling to the smallest set of tokens whose cumulative probability exceeds p. Setting `top_p=0.9` means only tokens comprising 90% of the probability mass are considered, even after temperature scaling.

## Example

Setting temperature=0 for a unit test generator ensures deterministic, syntactically correct code. Setting temperature=1.2 for a story-writing assistant produces more imaginative, unexpected narrative choices.
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Source: https://aimastermindlab.com/glossary/temperature — AI Mastermind Lab (https://aimastermindlab.com)