Choose AI feature metrics by starting with the user’s task and the consequences of failure—not with a model score that is easy to collect. Define what success means in the intended setting, then use a small portfolio of task-quality, safety, reliability, and operational measures. Test those measures before launch, review results across relevant groups and conditions, and keep monitoring after release.
Start with the feature’s job, not a convenient score
Write down who will use the feature, what they will do with it, the setting in which it will run, and what outcome the feature is meant to produce. “Drafts a response for an agent to review” is a different task from “sends a response without review”: the second has less human oversight and may require a stricter success threshold and stronger safeguards.
Before choosing metrics, agree on three outcome categories:
- Acceptable result: what the user needs the feature to get right.
- Partial success: what is useful but incomplete, and what human follow-up it requires.
- Unacceptable failure: what must not happen, such as an unsafe action or a materially wrong answer in a consequential setting.
Include domain specialists when the task depends on specialist judgment. For consequential uses, seek input from people affected by the output as well as end users. NIST’s guidance treats assessment as something to tailor to organizational objectives and context; its AI Risk Management Framework FAQ also notes that the relevance and importance of trustworthiness characteristics vary by setting and stakeholder. The FAQ describes the framework as a living document and notes a 2025 task to revise AI RMF 1.0, so it should not be treated here as confirmation of the latest framework revision.
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Build a small portfolio of measures
No single score captures whether an AI feature is useful, safe, and dependable in every setting. Start with a direct measure of the intended task, then add measures for material risks and operating constraints. NIST’s AI measurement and evaluation page says each trustworthiness characteristic needs its own portfolio of measurements and evaluations, and that context is crucial. Its examples of measurement areas include accuracy, robustness, bias, interpretability, and transparency; broader trustworthiness dimensions include privacy, reliability, safety, security, and mitigation of harmful bias.
| Feature or concern | Candidate measures | What to check |
|---|---|---|
| General response generation | Task correctness or completion; coherence; fluency | Does the response satisfy the user’s actual need, rather than merely sound plausible? |
| Retrieval-augmented generation (RAG) | Groundedness; relevance; task correctness | Are responses supported by relevant retrieved material, and do they answer the request? |
| Agent or tool-using workflow | Tool-call accuracy; task completion; safety of actions | Did the system select and use the intended tools correctly, and did it reach the permitted outcome? |
| Fairness and distribution of outcomes | Performance and error measures across relevant demographic groups and deployment segments | Do aggregate results conceal substantially different errors or impacts for a group, language, task type, or operating condition? |
| Service operation | Latency; token consumption; error rates; production quality; bug frequency and severity; time to response and repair | Do quality and service behavior stay within user needs and operational constraints? |
The task examples and operational signals in this table are drawn from Microsoft Foundry documentation; they are examples, not a universal standard or an independent endorsement of that product. The dimensions are not interchangeable: a high fluency score does not establish correctness, and an aggregate accuracy score does not establish safety, privacy, or equitable performance.
Prefer direct evidence over proxies
For the core task, use a measure close to the outcome whenever possible: task completion, correctness against a defensible reference, required-field validity, or successful execution of an intended action. A proxy—such as fluency, user satisfaction, or a model-judge score—can still be useful, but only if evidence shows it tracks the outcome you care about in the target setting.
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For example, a model-judge score may help sort a large set of responses for review, but it is not automatically a trustworthy substitute for expert assessment. Check it against human judgments on representative examples, inspect disagreements, and keep the underlying task outcome visible. If the intended task is an agent action, assess the action and its consequences rather than scoring only the explanation that accompanies it.
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Choose evaluation slices that reflect deployment
Report overall performance alongside results for segments likely to matter in actual use. Depending on the feature, these might include demographic groups, languages, task types, customer cohorts, or operating conditions. Select segments based on deployment context and plausible impacts rather than slicing data indiscriminately; excessive slicing can produce unstable estimates that are easy to misread.
NIST’s AI RMF Playbook recommends documenting performance and error metrics across demographic groups and other segments relevant to deployment, and considering feedback from end users and affected communities. Averages alone can hide a serious weakness for a particular group or condition. Where sample sizes are small, label that uncertainty instead of presenting a noisy result as a firm comparison.
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When comparing model or feature versions, keep the evaluation setup equivalent if the question is which version performs better under the same conditions. If the question is instead what each system can achieve with its best supported setup, say so and document the differences. OpenAI’s evaluation guidance distinguishes capability-elicitation, safeguard-performance, and comparison claims, and emphasizes describing the evaluation harness and evidence behind a claim.
Evaluate before release and monitor after release
Before launch
- Build an evaluation set that represents the intended users, tasks, and operating conditions.
- Include edge cases and failure-prone scenarios, not just typical examples.
- Assess robustness and relevant safety risks, including whether the feature behaves acceptably when inputs are ambiguous or unusual.
- Record the evaluation setup and compare versions under controlled, repeatable conditions.
After launch
- Sample production behavior to check whether real use matches pre-release results.
- Run scheduled evaluations on a stable test set to detect changes or drift over time.
- Monitor the operational signals that matter for the feature, such as latency, token consumption, and error rates, alongside quality and safety signals.
- Set alerts for meaningful quality-threshold failures or harmful outputs, and assign an owner to investigate them.
Microsoft Foundry documentation describes lifecycle practices such as quality and safety evaluators, custom evaluators, tracing, monitoring, and scheduled evaluations. Those features and their availability can change; treat the documentation as an example of an implementation approach, not proof that a particular tool is suitable for every team.
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A metric is useful only if the team can interpret a change and decide what to do about it. For each measure, document:
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- the outcome or risk it represents and the scoring rule, including the numerator and denominator when applicable;
- the data source, evaluation window, and relevant segments;
- the threshold and why that threshold is acceptable for this use;
- the person or team responsible for review; and
- the action triggered by a miss, such as investigation, rollback, a launch hold, or human review.
This definition makes it easier to rerun an evaluation and understand why results differ. A score without its dataset, setup, and decision rule is difficult to compare or use as evidence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check whether the evaluation itself can be trusted
Document the claim being tested, the setup or harness, the resources used, and why the evidence supports that claim. OpenAI’s evaluation guidance highlights threats that can make a result misleading, including reward hacking (the system exploits a scorer shortcut), refusals that mask the behavior under test, contamination from training or discoverable tasks, broken or unfair tasks and environments, and possible sandbagging. For multi-step systems that use tools, the harness can materially affect measured performance.
Also check whether a metric has become a target in a way that weakens the real outcome. A system can improve a score while failing users if the score rewards the wrong behavior. Review examples behind both improvements and regressions, particularly for safety-sensitive outcomes.
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Use results as evidence, not a guarantee
Metrics support decisions; they do not certify that a feature is trustworthy in every context. NIST cautions that addressing trustworthiness characteristics one at a time does not ensure overall trustworthiness: characteristics can interact, trade-offs occur, and their importance differs across settings and affected people.
NIST’s TEVV-Athlon announcement, published August 7, 2026, describes a draft framework for tailoring testing, evaluation, verification, and validation to organizational objectives. The announcement said the initial public-draft comment period ended October 6, 2026. It is draft guidance, not a final standard. NIST’s broader measurement page, which has no publication date shown, also describes its historical work as hundreds of evaluations of thousands of AI systems; that is a description of NIST’s activity, not a benchmark showing that any specific metric predicts success.
For teams without the expertise to build or review a specialized evaluation, independent AI measurement or responsible-AI assessment may be worth considering. Whether an outside service is appropriate depends on the feature, the risks, and the team’s own capabilities; no particular provider or rate is established here.
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