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To check whether an AI answer is grounded, review its important claims against the evidence, confirm that it preserves the evidence’s limits, judge it against the user’s task, and record whether each point is supported, needs qualification, or is unsupported. A polished answer or a list of citations is not enough: the sources must actually support the claims they are attached to.
The four-prompt rubric below is an editorial synthesis informed by NIST guidance. It is not a NIST rubric, validated scoring system, certification, or guarantee that an AI system is trustworthy.
A four-prompt rubric for reviewing a grounded AI answer
Use the prompts on the answer’s material claims, not just its overall tone. For each prompt, record one outcome: supported, needs qualification, or unsupported. These labels are a simple editorial implementation, not an official NIST scale or universal threshold.
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Evidence support
Are the answer’s important factual claims supported by the cited or supplied sources? Open the relevant source and check whether it says what the answer attributes to it. A citation that is merely related to the topic does not establish the specific claim.
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Faithful qualification
Does the answer retain relevant dates, limits, uncertainty, and distinctions from the evidence? Mark wording that makes a source’s finding broader or more certain than the source allows. A claim can point to a real source and still need qualification.
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Task and context fit
Does the answer address the user’s actual question for the intended audience, use, and setting? Note missing context that could change what a useful or safe answer would be. Judge usefulness against the person’s goal, not style alone.
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Traceability and review outcome
Can another reviewer trace key claims to the evidence behind them? Record unsupported or unclear claims and decide whether the answer needs correction, qualification, or escalation. If the answer is consequential, involve a reviewer with relevant expertise rather than relying on the short rubric alone.
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How to apply the rubric consistently
Keep a short record with the reviewed answer, its task and audience, the evaluation conditions, the rubric version, and the outcome for each prompt. When several people review answers, make the basis for their decisions visible: identify the claims checked, the sources consulted, and the reason for any correction or escalation. This makes it easier to revisit a decision when the answer, evidence, or use context changes.
NIST’s AI Risk Management Framework Core recommends documenting test sets, metrics, and tools used for testing, evaluation, verification, and validation (TEVV). It also calls for applicable protections and a representative relevant population when evaluations involve human subjects, and emphasizes assessing performance in conditions similar to deployment. Those principles support recording the review context; they do not prescribe this four-prompt checklist. NIST AI RMF Playbook
Why claim-level evidence and context matter
NIST’s agentic AI evaluation project describes comparing factual claims with a human-curated reference corpus and creating an audit trail to show where evidence came from and how it supports conclusions. That approach informs the rubric’s emphasis on checking individual claims and making their evidence traceable. NIST AI Agent Standards Initiative
Context matters too. NIST’s 2024 AI Use Taxonomy: A Human-Centered Approach describes 16 AI use activities and focuses on human goals and outcomes. For a reviewer, the practical implication is to assess whether an answer helps with the task at hand, not whether it merely sounds fluent. NIST, AI Use Taxonomy: A Human-Centered Approach
What NIST guidance does—and does not—establish
NIST describes the AI Risk Management Framework as voluntary guidance intended to improve the incorporation of trustworthiness considerations into the design, development, use, and evaluation of AI systems. The framework was released on January 26, 2023, and NIST says AI RMF 1.0 is being revised. Its voluntary status means this checklist should not be presented as a compliance test or certification. NIST AI Risk Management Framework
NIST’s ARIA 0.1 pilot report, published as NIST AI 700-2 in November 2025, describes scenario-based interactions between human testers and AI applications, alongside expert annotation and assessment data. It presents a pilot approach to evaluating real-world risks and impacts; it does not validate this rubric. NIST AI 700-2, ARIA 0.1 Pilot Report
NIST’s framework resources page says the framework was developed over 18 months with contributions from more than 240 organizations. That figure provides context for the framework’s development; it is not evidence that this specific checklist is effective. NIST AI RMF Resources
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When a short rubric is not enough
Use qualified reviewers and a fuller evaluation when errors could have significant consequences or when the task requires specialized expertise. A compact review can surface unsupported claims, missing qualifications, and context problems, but it cannot establish universal trustworthiness. The sources informing this rubric do not establish validated score cutoffs, measured inter-rater reliability for these four prompts, or a success rate for the rubric.
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