Comparison
Sampling vs Top-k
Sampling and Top-k 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-k
Top-k comes up when the question is fundamentally about inference.
top-k = 50: a common default in Hugging Face generation.
Frequently asked
What is the difference between Sampling and Top-k?
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-k: Top-k restricts token sampling to the k highest-probability tokens, then samples from that set. A simpler alternative to top-p.
When should I use Sampling vs Top-k?
Sampling is the right concept when you are focused on inference. Top-k applies when you are focused on inference.
Are Sampling and Top-k the same thing?
No. Sampling is inference; Top-k is inference. They are related but address different parts of the AI stack.