Temperature
Temperature is a setting used before sampling. It changes whether token chances are packed around a few choices or spread across more choices.
Another example
An AI product engineer runs the same headline prompt 50 times at 0.2 and 50 times at 0.9. The team compares how much the two groups vary, but temperature alone cannot promise creative or true answers.
Main parts
- 01logits
- 02temperature value
- 03rescaled distribution
Use it when
Use this term when you tune how concentrated the token choices are during sampling.
Do not use it when
Do not compare values from different providers unless you know their scales and default settings match.
Name used in code
softmax(logits / temperature) 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 Temperature to make AI behavior measurable and tied to evidence. User context: An AI product engineer runs the same headline prompt 50 times at 0.2 and 50 times at 0.9. The team compares how much the two groups vary, but temperature alone cannot promise creative or true answers. AI method or concept: Temperature. Why it fits: Use this term when you tune how concentrated the token choices are during sampling. Do not use it when: Do not compare values from different providers unless you know their scales and default settings match. 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 Temperature. Definition: Temperature is a setting used before sampling. It changes whether token chances are packed around a few choices or spread across more choices. 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.