Temperature vs Top-P: LLM Sampling Parameters Explained
Temperature vs top-p explained: what each sampling parameter does, how they interact, and settings for coding, creative writing, and classification.
Temperature and top-p are the two sampling knobs that control randomness in LLM output. Understanding them — and knowing when to adjust one versus the other — makes a measurable difference in output reliability.
What temperature does
Temperature scales the probability distribution before sampling. Low (near 0) makes output near-deterministic — the model picks the most likely tokens. High adds randomness — more creative, more mistakes.
What top-p does
Top-p (nucleus sampling) limits sampling to the smallest set of tokens whose cumulative probability exceeds p. At 0.9 the model samples from the top 90% of probability mass; lower values make it more conservative.
How they interact
They are different mechanisms. Generally, adjust one, not both: temperature reshapes the whole distribution; top-p cuts its tail. Best practice is to pick one knob and keep the other at default.
Settings by task
- Coding and data extraction: temperature 0-0.2.
- Classification and structured output: 0.
- Business writing: 0.3-0.6.
- Creative writing: 0.7-1.0.
- Ideation / brainstorming: 0.8-1.2.
FAQ
What's the difference between temperature and top-p?+
Temperature scales the entire probability distribution to add or remove randomness. Top-p truncates the distribution to its top probability mass. They alter sampling differently.
What temperature should I use for coding?+
Near 0 (0-0.2). Code needs determinism. Use higher values only when exploring creative approaches.
Should I set both temperature and top-p?+
Best practice is to tune one and leave the other default. Setting both can over-constrain or over-randomize output.
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