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Test an open-ended AI feature with a written rubric, not an exact-answer key. Define what a good response must do, which alternative responses are acceptable, and what counts as failure; then check that human reviewers or automated judges apply those criteria reliably. The result is meaningful only for the test cases, system configuration, and user context you actually evaluated.
Start with the decision the test needs to support
First decide what the evaluation will inform: a release gate, a comparison between system versions, or ongoing quality monitoring. Record the deployment setting, intended users, and likely consequences of a bad answer. These details determine which qualities matter and how strict the evaluation should be.
Evaluate the feature as users will encounter it—not just the underlying model. The tested system may include the model, prompts, tools, retrieval sources, and surrounding workflow. Changing any of these can change the result. NIST’s January 2026 initial public draft of AI 800-2 treats the protocol and evaluation setting as part of benchmark design.
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Include routine requests alongside ambiguous inputs, edge cases, and examples tied to known failure modes. Make sure cases reflect the feature’s intended users and context. A set made only of easy, polished prompts can produce reassuring scores without showing how the feature handles realistic variation.
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Keep evaluation examples separate from routine prompt tuning where practical. Reusing the same cases to adjust prompts and then report performance can make the result less informative about new inputs. Select the number and type of test items and repeated trials in light of the evaluation goal, statistical power, and available budget, as NIST AI 800-2 advises.
Write the rubric before scoring responses
For each case, define the dimensions that matter to the feature. Depending on the use, these may include correctness, completeness, relevance, safety, tone, format, and grounding. Specify unacceptable outcomes and describe how meaningfully different answers can still pass.
Use anchored rating levels or clear pass/fail criteria, ideally with examples that help reviewers apply them consistently. For an AI support-answer feature, for instance, a rubric could separately assess whether the response addresses the issue, avoids inventing account facts, gives a safe next step, and communicates clearly. A response can satisfy those requirements without matching a reference sentence.
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This is a practical way to operationalize subjective evaluation, not a universal template prescribed by NIST. NIST AI 800-2, an initial public draft, states: “Some test item formats do not have a programmatically gradable answer.” Its discussion points to subjective procedures such as written rubrics.
Match the scoring method to the quality being tested
Use exact, deterministic checks for properties with objectively testable requirements—for example, whether required JSON fields are present or a required link appears. Semantic qualities such as helpfulness or whether an answer addresses a user’s intent generally need human judgment, an AI judge, or a combination. A brittle exact-string comparison can reject a valid answer simply because it uses different wording.
An AI judge is part of the measurement system, not unquestioned ground truth. Check how it interprets the rubric and compare its ratings with human reviewers on representative examples. Inspect disagreements, especially cases where the judge may reward confident wording, penalize a valid alternative, or overlook a safety problem. Multiple judges or interrater-agreement measures can add useful evidence when the decision justifies the added effort. NIST AI 800-2 discusses how judge design can materially affect results.
Record the rubric version and judge configuration used for each evaluation. Agreement with human reviewers provides evidence about that judge on the material you tested; it does not establish that the judge is universally valid.
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If generation is nondeterministic, one response per test case may not reveal how often the feature succeeds or fails. When the evaluation budget allows, run multiple trials per item and report the number of runs and the observed variation. Repeated trials can reduce uncertainty and help distinguish consistent performance from occasional failure, but they also increase evaluation cost. NIST AI 800-2 discusses this trade-off.
For consequential release decisions, show uncertainty rather than treating a small score difference as decisive when results are noisy. Statistical approaches such as generalized linear mixed models can help estimate question difficulty and distinguish variation between questions from variation across repeated outcomes. They are options, not mandatory steps for every product evaluation, and their assumptions should be explained. NIST’s February 19, 2026 report announcement, updated March 18, 2026, describes this statistical-evaluation work.
Say exactly what the score represents
A score can describe performance on the specific benchmark cases, or it can aim to estimate performance on similar future questions. NIST AI 800-3 distinguishes these targets as benchmark accuracy and generalized accuracy. State which one you mean and how it was estimated; a result on a fixed test set does not automatically generalize to all users, tasks, languages, or deployment conditions.
Describe the evaluated population and conditions alongside the score: the test set, user or task context, system configuration, and any repeated-run setup. NIST’s terminology helps make the scope explicit, but the method you choose should fit the claim you need to support.
Keep evidence that makes results auditable
Preserve complete outputs and the information needed to interpret them: prompts, model and system versions, rubric and judge versions, evaluation-code revision, and summary statistics. Inspect parser failures separately from model failures; a valid response can be misclassified when an answer parser is too brittle. NIST AI 800-2 recommends preserving logs and version information so evaluations can be interpreted and reproduced.
For grounded or agentic features, evaluate more than whether an answer sounds plausible. Check whether cited sources support the claims (faithfulness), whether the response preserves the source’s relevant meaning (completeness), and whether the source is strong enough to justify the claim (sufficiency). Retain evidence connecting claims to their supporting material. NIST’s ongoing Building Evaluation Probes into Agentic AI project describes rubric-based probes and machine-readable audit trails.
Use a practical quality checklist
- Determinism: Which properties can be checked exactly, and which require judgment?
- Validity: Does the rubric measure qualities that matter in the feature’s real use?
- Agreement: Do human reviewers and automated judges interpret the criteria consistently?
- Coverage: Does the test set include realistic variation and important failure cases?
- Cost: Can the team afford the chosen number of items, reviewers, judges, and repeated runs?
- Scope: Is the score about this fixed set or intended to represent a broader class of requests?
- Traceability: Can a reviewer connect each result to its output, configuration, and source evidence?
There is no universal rubric or single metric for every AI feature. Choose criteria and evidence to match the intended use and the consequences of errors; report what was tested without making a broader claim than the evaluation supports.
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