Evaluation metrics become product acceptance criteria when each measurement is tied to an explicit decision rule: what is measured, under which conditions, which result counts as acceptable, and what happens when it does not. A score on its own is evaluation evidence, not an acceptance criterion.
The cited AI risk-management framework states that performance or assurance criteria may be qualitative or quantitative and should be demonstrated under conditions similar to deployment settings. The other cited evaluation material distinguishes quality metrics such as coherence and fluency, retrieval-augmented generation metrics such as groundedness and relevance, task-completion measures, and safety and security measures.
Define each acceptance decision separately
Different evaluation areas answer different product questions. They should not be treated as interchangeable or combined into an unexplained overall score.
| Evaluation area | Product question | What the acceptance rule must specify |
|---|---|---|
| Quality | What level of coherence and fluency is acceptable? | The evaluation cases, scoring method, approved pass condition, and action for a failed result |
| Retrieval | What level of groundedness and relevance is required? | How support and relevance are assessed, what counts as unsupported output, and the required pass condition |
| Task completion | What constitutes successful completion? | The task boundary, success conditions, permitted exceptions, and failure action |
| Safety and security | Which behaviors or outcomes are unacceptable? | The evaluation method, classification rules, decision threshold or standard, and required response |
These categories come from the cited evaluation material. Their product-specific pass conditions do not: the sources do not provide a universal value that can be copied into an acceptance decision.
Check the translation in five steps
1. Tie the metric to a defined product use
The criterion should identify the workflow, intended use, and failure being evaluated. A metric may be relevant in one context but insufficient in another.
For example, retrieval relevance cannot by itself establish that a task was completed, while task completion does not establish that the output was safe. The acceptance decision should therefore state which question each metric answers.
2. Preserve the meaning of the metric
The acceptance document should define:
- the output or outcome being assessed;
- the evaluation cases and conditions;
- the scoring or review procedure;
- how ambiguous or incomplete results are handled; and
- the unit used to interpret the result.
Without those definitions, two teams can report apparently similar metrics while making different decisions.
3. Add an approved pass rule
A quantitative threshold is only one possible pass rule. The cited risk-management material explicitly allows criteria to be qualitative or quantitative, so an acceptance rule may use either an approved value or a defined qualitative standard.
The product team must determine the applicable rule. The cited sources do not state the required threshold, baseline, test-set size, decision owner, or release schedule.
4. Demonstrate the result under deployment-like conditions
An evaluation conducted under conditions unlike deployment may not support an acceptance decision. The team should document how the test conditions resemble the intended setting and identify any important differences.
This is an application of the cited deployment-condition principle, not a claim that the source supplies a universal test protocol.
5. Attach a failure action
A complete criterion states what happens when the result does not meet the pass rule. Depending on the product decision, that action may require review, remediation, a narrower use condition, or rejection.
Without a defined consequence, a failed metric remains informational and does not reliably determine acceptance.
Use a complete criterion template
A practical internal criterion can take this form:
For the defined workflow and use, [metric] will be evaluated under [deployment-like conditions] using [evaluation method]. Acceptance requires [approved quantitative threshold or qualitative standard]. A non-pass result triggers [defined action], and the decision is recorded with [supporting evidence].
A statement such as “the quality score is acceptable” remains incomplete because it does not identify the workflow, evaluation conditions, scoring method, pass rule, or consequence of failure.
What the product team must still confirm
The cited materials provide measurement principles and evaluation categories, but they do not settle product-specific or legal questions. Before approval, the team must confirm:
- the intended use, exclusions, and unacceptable failure modes;
- whether the evaluation cases adequately reflect deployment conditions;
- the threshold or qualitative standard and the basis for approving it;
- how human review or scoring disagreements will be handled;
- whether safety and security findings require separate treatment;
- which role has authority to approve, reject, or restrict the use;
- whether legal, regulatory, privacy, security, or contractual requirements apply; and
- how the criterion will be reviewed after changes to the system, data, workflow, or operating conditions.
No metric name or cited framework should be presented as supplying those missing decisions by implication. The defensible translation is a combination of metric, scope, test conditions, pass rule, failure action, and accountable approval.