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Start with the feature’s job, not a model score
Before choosing metrics, describe the feature in terms of who uses it, what they are trying to do, where it will run, and what a successful outcome looks like. That description is the evaluation target. “Drafts a response for a support agent to review” is not the same task as “sends a response to a customer without review.” The second feature has a different success condition and a greater consequence if it gets something wrong.
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Write down what counts as a complete success, a partial success, and an unacceptable failure. If an answer must include a required field, define whether an omitted field makes the result invalid. If a workflow must take an action, specify which action and what evidence confirms it happened. For consequential uses, involve relevant domain experts and people affected by the output when setting those criteria. NIST’s AI Risk Management Framework materials emphasize that the importance of trustworthiness characteristics varies with the setting and stakeholders; NIST’s AI RMF Playbook also recommends considering feedback from end users and impacted communities.
NIST’s TEVV-Athlon framework, announced August 7, 2026, is a draft approach for tailoring testing, evaluation, verification, and validation to organizational objectives. Its initial public comment period ended October 6, 2026. It is guidance in draft form, not a final standard or a universal scoring recipe.
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Build a small portfolio of measures
Use direct measures of the intended outcome wherever possible, then add measures for the feature’s risks and operating constraints. A portfolio is more informative than a single composite score because different measures reveal different ways a feature can succeed or fail. NIST’s AI measurement guidance notes that each trustworthiness characteristic requires its own measurement approaches and that context is crucial.
| Measure family | What it can tell you | What to define or watch for |
|---|---|---|
| Task outcome | Whether users’ intended task was completed, or whether an answer or action was correct against a defensible reference. | Specify the success rule, reference, partial-credit rule, and unacceptable errors. A plausible-sounding response is not proof of correctness. |
| Output quality | Whether responses are coherent, fluent, relevant, or grounded in the available source material. | Choose qualities that matter to the feature. Fluency alone does not establish factual accuracy or usefulness. |
| Safety and trustworthiness | Whether the feature behaves acceptably in areas such as robustness, privacy, reliability, security, interpretability, transparency, and harmful-bias mitigation. | Prioritize characteristics according to the use case, affected people, and failure consequences; no one measure covers them all. |
| Distributional performance | Whether overall results conceal materially different performance or errors across relevant groups or conditions. | Choose deployment-relevant segments, such as languages, task types, demographic groups, or operating conditions, and examine both performance and errors. |
| Operational performance | Whether the feature meets practical service constraints and remains usable in operation. | Depending on the product, track latency, token consumption, error rates, bug frequency and severity, response time, and time to repair. |
These categories are not a prescribed ranking or a checklist that every feature must satisfy in full. NIST’s AI RMF FAQ cautions that trustworthiness characteristics interact: attending to them one at a time does not guarantee that the system as a whole is trustworthy, and trade-offs may arise.
Match the metric to the feature type
The measure should test the behavior the feature actually performs. Microsoft Foundry documentation provides examples of task-specific evaluators; they are useful illustrations, not a universal standard or independent proof that a feature is effective.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match- General response generation: coherence and fluency can help assess readability and organization. Pair them with a direct check of correctness or task success where those outcomes matter.
- Retrieval-augmented generation (RAG): groundedness can assess whether the answer is supported by retrieved material, while relevance can assess whether it addresses the user’s question. Neither measure alone establishes that the retrieved material is complete or correct.
- Agentic workflows: tool-call accuracy can assess whether the agent selected and used tools appropriately; task completion can test whether the intended workflow outcome was reached. For multi-step work, define what counts as success across the full sequence, not just a successful individual call.
- Structured outputs or actions: required-field validity or successful execution of the intended action may be more direct than a general language-quality score. Specify the exact schema or action and how the result will be verified.
Be cautious with user satisfaction, fluency, or model-judge scores when using them as proxies for correctness. A proxy is useful only when there is evidence that it tracks the outcome you care about in the target setting. A model-judge score should therefore have a clear scoring rule and validation against appropriate human judgments or other defensible references; it should not silently become the definition of success.
Choose segments that reflect real deployment
Report overall results alongside results for the groups and conditions that could materially change performance or impact. NIST’s AI RMF Playbook recommends documenting performance and error metrics across demographic groups and other segments relevant to deployment, as well as considering feedback from end users and impacted communities.
Possible segments include language, task type, customer cohort, demographic group, or operating condition—but include a segment because it is relevant to the deployment and likely impacts, not simply because it is available in the data. Decide in advance how you will interpret a gap, what level of evidence is meaningful, and who will review it. A good overall average can coexist with a serious weakness in a subgroup or an uncommon but consequential task.
Evaluate before launch and monitor after it
Before launch
Use a representative evaluation set that reflects the intended tasks and users, and include edge cases and conditions in which the feature may fail. Assess robustness and safety as well as ordinary task performance. Keep the setup consistent enough that results can be reproduced and, when comparing versions, make the conditions equivalent unless the claim is explicitly about the best supported configuration of each system.
After launch
Monitor sampled production behavior and relevant operational signals, and run scheduled evaluations on test sets to look for quality changes over time. Microsoft Foundry documentation describes lifecycle practices such as production monitoring, scheduled evaluations, tracing, and quality and safety evaluators. Its documented operational signals include latency, token consumption, and error rates. Treat these as examples of available evaluation and observability practices, not as a requirement to use a particular product.
For each metric, record its numerator and denominator or scoring rule, data source, evaluation window, relevant segments, threshold and rationale, owner, and the action triggered by a miss. For example, a threshold that raises an alert but has no assigned reviewer or response process is not an operational control. Set thresholds according to the feature’s purpose and risk rather than borrowing a convenient number from another system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make comparisons and evaluation claims reproducible
A result is only as useful as the claim it supports. State whether the evaluation is intended to measure capability, safeguard performance, or a comparison between systems or versions; those claims require clearly described conditions. OpenAI’s evaluation guidance emphasizes documenting the evaluation harness and the evidence behind a claim. For a comparison, keep prompts, tools, data, scoring rules, and other relevant conditions equivalent unless the comparison explicitly concerns each system’s best-supported setup.
Document the test data, scorer or evaluator, harness, tools and resources available, and the criteria used to judge the result. Explain why those choices provide valid evidence for the stated task. Check for common threats to confidence:
- Reward hacking: the system may exploit a shortcut in the scoring rule without achieving the user’s intended outcome.
- Refusals that mask behavior: a model may refuse a test prompt, so the evaluation does not reveal how it would behave on the underlying task.
- Contamination: test material may have appeared in training data or be discoverable, making performance look stronger than generalization warrants.
- Broken or unfair tasks and environments: a task, tool, or environment may fail in a way that invalidates the result or disadvantages a system for reasons unrelated to the intended capability.
- Sandbagging: a system may underperform in an evaluation setting, complicating the interpretation of a low score.
For tool-using and multi-step systems, the harness can materially affect measured performance. A benchmark score should therefore be read as evidence from a particular setup—not as a guarantee about every user, task, or production condition.
Use the results to make a decision
Before testing, connect each measure to a decision: launch, restrict, revise, or investigate. A metric with no clear action attached may be interesting, but it is less useful for managing the feature. Review task success together with relevant safety, segment, and operational results; a high average should not override a failure that the team has already defined as unacceptable.
When results are mixed, identify which dimension failed and whether the evaluation itself was valid before changing the feature or relaxing a threshold. Keep the claim narrow enough to match the evidence: a successful test on a particular task set supports a claim about that setup, not a blanket claim that the AI feature is accurate, safe, or trustworthy in all contexts.
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