Sampling Parameters
Settings that influence how predictable or varied a model's generated tokens are.
Plain English
They are knobs for making output more focused or more surprising.
Example
A classifier uses low temperature for consistency, while a brainstorming tool may allow more variation.
Why it matters
Sampling settings shape reliability, creativity, reproducibility, and debugging expectations.