Comparison
Sampling vs Top-p
Sampling and Top-p are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Sampling
Sampling comes up when the question is fundamentally about inference.
OpenAI default: temperature 1.0, top-p 1.0.
When you would reach for Top-p
Top-p comes up when the question is fundamentally about inference.
top-p = 0.9: typical for chat assistants.
Frequently asked
What is the difference between Sampling and Top-p?
Sampling: Sampling is the act of choosing the next token from the model's output distribution, typically after applying temperature and a truncation strategy like top-p or top-k. Top-p: Top-p (nucleus sampling) restricts token selection to the smallest set of tokens whose cumulative probability reaches p. Common values are 0.9-0.95.
When should I use Sampling vs Top-p?
Sampling is the right concept when you are focused on inference. Top-p applies when you are focused on inference.
Are Sampling and Top-p the same thing?
No. Sampling is inference; Top-p is inference. They are related but address different parts of the AI stack.