Restricting sampling to the smallest set of tokens whose probability sums to p.
1 min readupdated 2026-07-04
/ quick answer
Top-p sampling picks from the smallest set of highest-probability tokens whose cumulative probability exceeds p (e.g. 0.9), producing more coherent output than pure temperature scaling. Restricting sampling to the smallest set of tokens whose probability sums to p.
Restricting sampling to the smallest set of tokens whose probability sums to p. Top-p sampling picks from the smallest set of highest-probability tokens whose cumulative probability exceeds p (e.g. 0.9), producing more coherent output than pure temperature scaling. In practice: top_p=0.9 with temperature=0.7 is a common default for creative writing. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
Top-p sampling picks from the smallest set of highest-probability tokens whose cumulative probability exceeds p (e.g. 0.9), producing more coherent output than pure temperature scaling.
Example
top_p=0.9 with temperature=0.7 is a common default for creative writing.
/ frequently asked
What is Top-p (Nucleus Sampling)?
Top-p sampling picks from the smallest set of highest-probability tokens whose cumulative probability exceeds p (e.g. 0.9), producing more coherent output than pure temperature scaling.
What is an example of Top-p (Nucleus Sampling)?
top_p=0.9 with temperature=0.7 is a common default for creative writing.
Why does Top-p (Nucleus Sampling) matter for AI and automation?
Restricting sampling to the smallest set of tokens whose probability sums to p. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.