Comparison
Sampling vs Temperature
Sampling and Temperature 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 Temperature
Low for code/extraction; medium for chat; high for creative writing.
Temperature 0: same prompt, same response, every time.
Frequently asked
What is the difference between Sampling and Temperature?
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. Temperature: Temperature is a generation parameter that controls randomness. 0 is deterministic (always pick the most likely token); higher values produce more diverse, surprising output.
When should I use Sampling vs Temperature?
Sampling is the right concept when you are focused on inference. Low for code/extraction; medium for chat; high for creative writing.
Are Sampling and Temperature the same thing?
No. Sampling is inference; Temperature is inference. They are related but address different parts of the AI stack.