Sampling
Sampling means choosing the next text piece (token) from a list of possible choices and their chances. The model does not always pick the most likely one.
Another example
A conversation designer runs the same travel prompt with one random seed and one set of sampling rules. The answers can vary within those rules. Always picking the most likely token is greedy decoding, not sampling.
Main parts
- 01token probabilities
- 02sampling policy
- 03random seed
Use it when
Use this term when you control how much token generation can vary by chance.
Do not use it when
Do not say sampling makes facts more accurate or removes the need to test answers.
Name used in code
sample(next_token_distribution) Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Sampling to make AI behavior measurable and tied to evidence. User context: A conversation designer runs the same travel prompt with one random seed and one set of sampling rules. The answers can vary within those rules. Always picking the most likely token is greedy decoding, not sampling. AI method or concept: Sampling. Why it fits: Use this term when you control how much token generation can vary by chance. Do not use it when: Do not say sampling makes facts more accurate or removes the need to test answers. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): It is official for that source. Other platforms or teams may use the term in another way. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Sampling. Definition: Sampling means choosing the next text piece (token) from a list of possible choices and their chances. The model does not always pick the most likely one. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.