Top-p Sampling
Top-p sampling sorts possible tokens by chance, then keeps the smallest group whose total chance reaches p. It picks the next token from that group.
Another example
The next-token chances add up to 1.0, and an engineer sets top-p to 0.9. Only the smallest group that reaches 0.9 can be picked. Unlike top-k, this group does not have a fixed number of tokens.
Main parts
- 01ranked candidates
- 02cumulative threshold
- 03eligible nucleus
Use it when
Use this term when cumulative chance sets a changing limit on possible next tokens.
Do not use it when
Do not call top-p a quality score or a confidence limit for the whole answer.
Name used in code
cumulative_probability >= p 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 Top-p Sampling to make AI behavior measurable and tied to evidence. User context: The next-token chances add up to 1.0, and an engineer sets top-p to 0.9. Only the smallest group that reaches 0.9 can be picked. Unlike top-k, this group does not have a fixed number of tokens. AI method or concept: Top-p Sampling. Why it fits: Use this term when cumulative chance sets a changing limit on possible next tokens. Do not use it when: Do not call top-p a quality score or a confidence limit for the whole answer. 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 Top-p Sampling. Definition: Top-p sampling sorts possible tokens by chance, then keeps the smallest group whose total chance reaches p. It picks the next token from that group. 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.