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A compact rubric for reviewing an AI answer
Review the answer against the evidence it relies on, not against how confident or polished it sounds. Apply each prompt to the answer’s important claims and use one outcome: supported, needs qualification, or unsupported. These labels are a proposed way to record a human review; NIST has not established them as an official scale.
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- Evidence support: Are the important factual claims supported by the cited or supplied sources? Does the cited material actually say what the answer claims?
- Faithful qualification: Does the answer preserve relevant limits, dates, uncertainty, and distinctions in the evidence, or does it overstate what the sources establish?
- Task and context fit: Does it answer the user’s question for the intended use, audience, and setting? What missing context could change the answer?
- Traceability and review outcome: Can another reviewer trace key claims to evidence? Record unsupported or unclear claims and whether the answer needs correction, qualification, or escalation.
How do I check if an AI answer is grounded?
Trace claims, not just citations
A bibliography or a list of links is not enough. For each consequential claim, identify the source passage or other supplied evidence that supports it, then compare the claim’s meaning with the evidence. NIST’s agentic AI evaluation project describes comparing factual claims with a human-curated reference corpus and creating an audit trail showing where evidence came from and how it supports conclusions. That practice motivates making claim-to-source traceability central to a review; it does not validate this four-prompt rubric. NIST’s agentic AI evaluation project
Check the strength and limits of the wording
A source may support a narrower statement than the answer makes. Check whether the answer preserves dates, uncertainty, scope, and distinctions in the evidence. If support is partial, mark the claim as needing qualification and note what wording or limitation is missing. If the cited material does not support the claim, mark it unsupported rather than treating a plausible-sounding answer as established.
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How can a human review an AI answer for factual accuracy and usefulness?
Judge it against the person’s task
Factual support is necessary, but usefulness also depends on what the person is trying to do. A response may be accurate yet incomplete or unsuitable for its audience, setting, or intended use. NIST’s 2024 AI Use Taxonomy: A Human-Centered Approach describes 16 AI use activities and emphasizes human goals and outcomes. Use that perspective to ask whether the answer helps with the actual task, rather than judging it on style alone. NIST, AI Use Taxonomy: A Human-Centered Approach (2024)
Record review conditions
Note the task, intended audience, evaluation conditions, relevant criteria, and rubric version alongside the review outcome. NIST’s AI Risk Management Framework (AI RMF) Core recommends documenting test sets, metrics, and testing, evaluation, verification, and validation tools; it also calls for performance criteria to be demonstrated under conditions similar to deployment. For evaluations involving human subjects, it points to applicable protections and a representative relevant population. These ideas support documenting the conditions behind a review rather than treating its result as context-free. NIST AI RMF Core
What should happen when a claim is unclear or high-impact?
Record which claim is in question, what evidence was available, and what action is needed. Correct an unsupported claim, add a qualification when the evidence is narrower than the wording, and escalate when the reviewer cannot responsibly resolve the issue or the consequences warrant qualified expertise. For high-impact use, involve qualified reviewers who understand the relevant consequences and context; a short checklist cannot establish that an AI system is trustworthy.
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Human evaluation can also involve scenario-based interactions and expert annotation. NIST’s ARIA 0.1 pilot report, NIST AI 700-2, published in November 2025, describes this as a pilot approach to evaluating real-world risks and impacts. It is an example of a broader evaluation method, not evidence that the four prompts here have been validated. NIST AI 700-2, ARIA 0.1 Pilot Report (November 2025)
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What this rubric can—and cannot—tell you
The four prompts offer a small, repeatable way to identify evidence gaps, overstated claims, task mismatches, and review actions. The sources cited here do not establish universal scoring cutoffs, measured inter-rater reliability for this rubric, or a success rate. Treat its labels as review notes, not a trustworthiness score.
NIST describes its AI RMF as voluntary, intended to improve the incorporation of trustworthiness considerations into AI design, development, use, and evaluation; its framework page says AI RMF 1.0 is being revised. The framework is guidance, not a certification, and using this rubric does not establish compliance or guarantee that an answer or system is trustworthy. NIST AI Risk Management Framework
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