AI Evaluation
AI evaluation is a repeatable way to test an AI system. It uses clear tasks, test data, scoring rules, and safety limits.
Another example
A team tests candidate models with 300 saved support cases and answers approved by people. It scores quality, safety, delay, and when a model refuses to answer. An impressive demo alone is not an evaluation.
Main parts
- 01versioned dataset
- 02scoring rubric
- 03release threshold
Use it when
Use this term when clear rules are used to compare models, prompts, search changes, or releases.
Do not use it when
Do not shrink an evaluation to one total score or a few prompts with no saved version.
Name used in code
dataset × rubric × evaluator × threshold 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 AI Evaluation to make AI behavior measurable and tied to evidence. User context: A team tests candidate models with 300 saved support cases and answers approved by people. It scores quality, safety, delay, and when a model refuses to answer. An impressive demo alone is not an evaluation. AI method or concept: AI Evaluation. Why it fits: Use this term when clear rules are used to compare models, prompts, search changes, or releases. Do not use it when: Do not shrink an evaluation to one total score or a few prompts with no saved version. 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 AI Evaluation. Definition: AI evaluation is a repeatable way to test an AI system. It uses clear tasks, test data, scoring rules, and safety limits. 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.