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Start with the decision the test needs to support
Before collecting outputs, write down what decision the evaluation will inform: for example, whether a feature is ready to launch, whether a change improved it, or whether a known failure has been fixed. Specify the intended users, deployment setting, and consequences of a bad response. Those details determine which qualities matter and how much evidence you need.
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Evaluate the system users will actually encounter, not just the underlying model. The relevant system may include the model, prompts, tools, and surrounding workflow; changing one of these can change what an evaluation result means. NIST’s Practices for Automated Benchmark Evaluations of Language Models treats protocol and setting choices as part of benchmark design. The document is an initial public draft published in January 2026, so its guidance may change: NIST AI 800-2 draft.
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Include a mix of ordinary requests and cases that are more likely to expose weaknesses. A set made only of clean, typical prompts can make a feature look reliable while missing the conditions that matter in practice.
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- Common requests from the feature’s intended audience.
- Ambiguous or underspecified requests, where a useful response may need to ask a question or state an assumption.
- Edge cases and known failure modes, including requests that test safety or factual restraint.
- Variation in wording and context that the feature is expected to handle.
Keep evaluation examples separate from routine prompt tuning where possible, so the team does not merely optimize against the cases it later reports. NIST AI 800-2 says test-item and trial choices should reflect evaluation goals, statistical power, and budget; it does not prescribe one universal test-set size.
Write the rubric before judging responses
A rubric makes room for multiple correct answers by judging properties that matter instead of requiring the model to reproduce one preferred sentence. Choose criteria for the feature’s actual use, then define what a pass, partial result, or failure looks like. NIST notes that some test item formats cannot be graded programmatically and need a subjective procedure such as a written rubric.
For a support-answer feature, for example, separate whether the response addresses the issue, avoids inventing account details, gives a safe next step, and communicates clearly. One answer may explain a solution in a different order or wording from another and still pass. The rubric should also make clear when a response fails—for instance, if it asserts an account fact that was never provided.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteUse anchored levels or pass/fail criteria, with examples where reviewers might otherwise disagree. The dimensions and anchors below are an implementation pattern, not a template mandated by NIST:
- Correctness: Are factual claims accurate for the information available?
- Completeness: Does the response cover what the user needs for this task?
- Relevance: Does it address the request rather than drift into adjacent topics?
- Safety: Does it avoid harmful guidance or unsupported certainty where the stakes require caution?
- Format and tone: Does it meet any required format and communicate appropriately?
- Grounding: If the feature uses sources, are its claims supported by them?
Not every feature needs every dimension. A strict JSON-output feature may need a deterministic format check; a drafting assistant may need a more nuanced relevance and usefulness rubric. There is no single rubric or metric suitable for every AI feature.
Choose scorers that fit the criteria
Use exact code for properties with unambiguous answers, such as whether required JSON fields exist or a required link is present. For semantic qualities, use trained human reviewers, an AI judge, or a combination. Do not treat an automated judge as unquestioned ground truth: its interpretation of the rubric can affect the score.
Check an AI judge against human ratings on representative outputs. Review disagreements and cases where the judge might reward confident phrasing, reject a valid alternative, or overlook a safety problem. If the result will drive a consequential decision, consider multiple judges or measures of agreement between raters. Agreement on the tested material is evidence about those judges and cases—not proof that the rubric is universally valid.
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Record the rubric version and judge configuration alongside the scores. NIST AI 800-2 discusses how LLM-as-a-judge design and subjective scoring affect evaluation: NIST AI 800-2 draft.
Account for variation between runs
Generative systems can produce different outputs for the same test item. When that variation matters and the evaluation budget allows, run an item more than once and report how many trials you used and how much the results varied. Repeated trials can help distinguish a response that is consistently acceptable from one that sometimes fails, but they increase evaluation cost.
More trials can reduce uncertainty and help quantify variation due to sampling; they do not make a weak test set representative of users it does not cover. NIST AI 800-2 discusses the trade-off between trials, uncertainty, and cost in its draft guidance.
Say what your score represents
A score can describe performance on the exact cases tested, or it can aim to estimate performance on a broader population of similar future requests. These are different claims. NIST AI 800-3 calls them benchmark accuracy and generalized accuracy, respectively, and emphasizes making the target and estimation method explicit. See NIST’s February 19, 2026 report announcement, updated March 18, 2026.
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For a fixed test set, report the result as performance on those cases. To make a broader claim, explain how the cases represent the requests you care about and how the estimate was obtained. Results from one benchmark do not automatically apply to all users, tasks, languages, or deployment conditions.
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When the decision is consequential, show uncertainty rather than presenting a small score difference as decisive if the evaluation is noisy. Statistical options such as generalized linear mixed models can help estimate differences in question difficulty and variation across outcomes, but they involve assumptions that should be explained. They are methodological options, not a required step for every product evaluation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Preserve outputs and troubleshoot the measurement
A score is only useful if you can tell what produced it and investigate apparent failures. Keep the material needed to reproduce and interpret the evaluation:
- Complete prompts and model outputs, including failed or malformed responses.
- Model and system versions, plus relevant prompt, tool, and workflow configurations.
- The rubric version, judge instructions and configuration, and human ratings.
- Evaluation code revision and summary statistics, including the number of trials and reported variation.
Separate failures in the feature from failures in the measurement pipeline. A brittle parser may reject a semantically correct answer because it uses a valid alternative format; inspect parser failures rather than counting each one automatically as a model failure.
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For grounded or agentic features, assess more than whether an answer sounds plausible. Check whether citations support the claims (faithfulness), whether the answer preserves the source’s relevant meaning (completeness), and whether the source is strong enough to support the claim (sufficiency). Retain evidence connecting claims to the supporting material so reviewers can audit a score.
NIST’s ongoing project on building evaluation probes into agentic AI describes rubric-based probes and machine-readable audit trails for this kind of evaluation.
A practical release check
Before using results to make a release or quality decision, confirm that the evaluation answers these questions:
Quick Recap
- Is the tested system and deployment context the one the decision concerns?
- Does the test set cover ordinary use, important variation, and known failure modes?
- Does the rubric permit distinct valid answers while defining meaningful failures?
- Have semantic scorers been checked for consistency against human judgments?
- Are repeated-run variation and the number of trials visible where they matter?
- Does the report distinguish test-set performance from estimates about future requests?
- Can the team trace scores back to outputs, configurations, code, and source evidence?
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