Test an AI system with evidence, not eloquence. Build a documented set of representative tasks, define the expected answers and acceptable uncertainty, compare factual claims with reliable ground truth, verify every citation, probe consistency across turns, and test groups and contexts that may receive different outcomes. Combine automated scoring with structured human red-teaming, record the exact system and conditions, and repeat the tests after material changes. A benchmark score can describe one evaluation setup; it cannot by itself prove that a product is reliable in your deployment.
What counts as a hallucination?
“Hallucination” is the familiar term, but the NIST Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (AI 600-1) uses confabulation. NIST defines it as content that a generative-AI system generates and confidently presents even though it is erroneous or false. The definition is broader than an isolated wrong fact: it also covers an answer that diverges from the prompt or other input, or that contradicts an earlier statement in the same conversation.
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That definition gives a practical testing target. You are looking for unsupported or false claims, invented references, omissions that change the answer, contradictions, failure to follow the question, and unjustified confidence. A polished paragraph is not evidence of accuracy. Nor is a disclaimer automatically evidence of safe uncertainty: the system may hedge while still making a false central claim.
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Why a benchmark score is not enough
Public benchmarks can be useful baselines, but they answer only the questions, under the prompts, languages, data, and scoring rules used by that benchmark. A product may add retrieval, tools, system instructions, filters, memory, or a human handoff that changes its behavior. The same base model can therefore perform differently in two applications.
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NIST advises against extrapolating capabilities from “narrow, non-systematic, and anecdotal assessments.” Treat a benchmark as one piece of evidence. A defensible decision also needs application-specific tasks, ground truth, expert review, and monitoring after launch. Document the benchmark’s assumptions, its known limitations, the possibility that test items appeared in training data (contamination), and how closely its conditions resemble your intended deployment.
Step 1: Define exactly what you are testing
Write a one-page test charter before collecting prompts. The unit under test is the complete system a user will encounter, not merely a model name.
- Model and version: record the provider’s model identifier, release or snapshot, and any routing or fallback models.
- Instructions and controls: preserve the system prompt, developer instructions, safety policies, temperature or sampling settings, token limits, memory behavior, and output formatting rules.
- Knowledge and tools: list retrieval indexes and their dates, connected databases, browsing or code tools, tool permissions, and timeout or error behavior.
- Workflow: describe authentication, preprocessing, post-processing, escalation to people, and what the user is allowed to ask.
- Users and context: state the languages, locations, roles, accessibility needs, and decision context the system is intended to serve.
- Consequences: classify what a wrong answer can cause—minor inconvenience, financial loss, privacy exposure, denial of a service, physical harm, or another high-consequence outcome.
Define a pass condition for each task before running it. “Sounds right” is not a criterion. For a factual answer, the condition might require all material claims to match an identified reference and uncertainty to be disclosed when the reference is incomplete. For a workflow, it may require the correct action, a safe refusal, or escalation to a human.
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Use real, de-identified cases where possible, supplemented with realistic cases written by people who understand the work. Preserve the provenance of every item: who supplied it, which period it represents, and whether it is public, synthetic, or derived from a production incident. Do not place confidential personal data in a test set unless your governance process explicitly permits it.
Cover routine and difficult requests
Start with the questions users ask most often, then add cases that expose failure modes:
- ordinary, well-specified questions with a known answer;
- ambiguous or underspecified requests that should trigger a clarifying question;
- prompts containing an unsupported premise, such as a nonexistent policy or event;
- requests beyond the system’s stated knowledge or tool access;
- incomplete, conflicting, or time-sensitive information;
- multi-turn follow-ups that refer to earlier claims;
- format-constrained tasks where a missing field matters;
- adversarial wording, misleading instructions, or attempts to override the intended rules.
For every item, store the prompt, the relevant conversation history, any supplied documents, the expected answer or acceptable answer range, required citations, prohibited claims, and the scoring rule. If several answers are valid, have a qualified reviewer write the acceptance criteria rather than forcing one exact sentence.
Include uncertainty and abstention cases
A reliable system must know when it lacks evidence. Add prompts whose correct behavior is to say that the information is unavailable, ask for a missing detail, consult an approved source, or hand the case to a person. Include near-neighbor pairs in which one question is answerable and the other is not. This reveals whether the system distinguishes evidence from plausibility instead of answering both with equal confidence.
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Keep a regression corpus
Save every serious failure as a permanent test. Tag it by cause—fabricated fact, unsupported citation, omission, contradiction, instruction failure, bias, unsafe advice, tool error, or another category you define. A regression corpus turns an incident into a repeatable check after a prompt edit, model update, retrieval refresh, tool change, guardrail change, or deployment change.
Step 3: Measure factuality and citation support
For claims that can be checked, compare the output with a trusted reference or another documented ground-truth record. Ground truth can be an authoritative policy, a dated technical specification, a verified database record, or a domain expert’s adjudicated answer. Record the reference version and date because facts can change.
Score claims, not just whole answers
Break a response into material claims and label each one:
- Supported: the reference directly establishes the claim.
- Contradicted: the reference shows it is false.
- Unsupported: the reference does not establish it, even if it might be true.
- Omitted: a required fact or qualification is missing.
- Unclear: the wording cannot be reliably judged without clarification.
Keep separate counts for invented facts, wrong dates or numbers, omissions, contradictions, and failure to express uncertainty. A single whole-answer score can hide a dangerous false statement among many correct sentences. For high-impact decisions, define which claims are “material” and require all material claims to pass rather than allowing harmless details to offset a critical error.
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- Existence: does the cited document, URL, section, or identifier actually exist?
- Entailment: does it support the specific statement made, rather than merely discussing the general topic?
- Suitability: is it an appropriate and sufficiently current source for this question and jurisdiction?
Mark a citation as failed if it is invented, points to the wrong document, cannot be retrieved, or is real but does not support the claim. NIST specifically recommends reviewing sources and citations in pre-deployment measurement and ongoing monitoring. A response can therefore be factually correct by chance yet still fail a citation-grounding requirement.
Check source-use behavior
Run paired tests with the same question when an approved source is available, unavailable, outdated, or contradictory. The system should use the supplied evidence when instructed, identify conflicts instead of silently choosing one, and disclose when it cannot verify a claim. Capture retrieval failures separately from generation failures so a broken index is not misdiagnosed as a model hallucination.
Step 4: Test consistency across turns and runs
Confabulation includes contradictions within the same context. Use conversation scripts rather than isolated prompts:
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- Ask for a fact and record the answer and stated confidence.
- Provide a relevant document or correction and ask the system to reassess.
- Ask a follow-up that depends on the first answer.
- Introduce a conflicting instruction or an incomplete detail.
- Ask the system to summarize its position and identify what remains uncertain.
Flag a contradiction when the system changes a material fact without acknowledging why. Also test repeatability: run the same case several times under the production sampling settings and compare whether the answer, citations, and uncertainty behavior remain within the accepted range. If randomness is intentional, report the distribution of outcomes and the worst observed failure, not only the most common answer.
Step 5: Test blind spots, bias, and unequal outcomes
Begin with an impact analysis: which demographic groups, languages, locations, disabilities, occupations, or other subgroups could receive different quality or decisions in this use case? Do not assume that a generic language-model benchmark measures fairness for a system that ranks applicants, allocates resources, moderates content, or gives health or financial guidance. The relevant measure may be domain-specific.
Use matched and counterfactual prompts
Create paired prompts that differ only in the attribute you are testing, while keeping the underlying facts constant. Add low-context prompts in which the system must not infer protected or sensitive characteristics from a name, dialect, location, or sparse biography. Counterfactual tests can reveal changes in recommendation, refusal, tone, detail, or factual accuracy that an aggregate score hides.
Include intersectional subgroups when the deployment could affect them; averages across broad categories can conceal a severe failure for a smaller group. Test the languages and varieties users will actually use, including translation and code-switching if those are part of the product.
Interpret differences carefully
A difference is a signal for investigation, not proof of a cause. Review the prompts, references, and downstream workflow for confounding factors. Check whether the system had less information for one group, whether the ground truth itself encodes a historical disparity, and whether the outcome measure is appropriate. Document groups not tested, small sample sizes, and possible train/test contamination. Do not publish a universal “bias-free” claim from a narrow paired test.
Step 6: Run a structured red-team test
Red-teaming is deliberate probing for realistic failures, not a random collection of clever prompts. Assemble testers with different backgrounds and include domain specialists for high-consequence uses. Give them the deployment charter, permitted tools, user roles, and escalation rules so they can test the system that will actually be used.
Organize probes by failure mode
- Misleading premises: ask the system to accept a false event, person, policy, or technical constraint.
- Low-context wording: remove details a careful answer needs and see whether it asks rather than guesses.
- Conflicting instructions: combine user, document, and tool instructions that should be resolved by the system’s hierarchy.
- Edge cases: use boundary dates, empty fields, unusual units, rare languages, and malformed inputs.
- Evidence conflicts: provide two sources with different dates or conclusions and test whether the conflict is surfaced.
- Harmful or discriminatory requests: assess refusal, safe redirection, and whether comparable benign requests are treated consistently.
- Tool and retrieval failures: simulate missing permissions, stale records, timeouts, and partial results.
For every probe, record the setup, expected safe behavior, observed response, severity, reproducibility, and recommended mitigation. Re-run high-severity probes with independent testers to reduce the chance that one person’s interpretation determines the result. Analyze patterns and root causes; a prompt list or automated score is not a complete risk assessment. NIST emphasizes that the team’s domain knowledge and awareness of deployment context affect the usefulness of red-team results.
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Choose methods by the question they answer
No single method detects every blind spot. Select a combination using the following axes.
| Method | Best question | Evidence produced | Main limitation | When human review is essential |
|---|---|---|---|---|
| Ground-truth factuality set | Are verifiable claims correct? | Claim-level comparison with dated references | Requires reliable, maintained ground truth | When claims are ambiguous, consequential, or disputed |
| Citation audit | Does each source support the statement? | Existence, entailment, and suitability judgments | A real source can still be outdated or misapplied | When legal, medical, policy, or jurisdictional context matters |
| Multi-turn consistency test | Does the system remain coherent after follow-ups or corrections? | Conversation traces and contradiction labels | Results depend on history and sampling settings | When a change of answer could alter a decision |
| Counterfactual or subgroup test | Do quality or outcomes differ across relevant groups? | Paired outputs and subgroup metrics | May miss intersectional or downstream effects | When the output affects people, eligibility, ranking, or access |
| Structured red team | How does the system fail under realistic pressure? | Adversarial cases, severity ratings, and mitigations | Coverage depends on tester expertise and scenario design | For all high-impact deployments |
| Production monitoring | What failures occur with real users and changing data? | Reports, sampled traces, drift and incident trends | Privacy, selection bias, and delayed detection | When deciding whether a recurring issue is safe to accept |
Set scoring rules that support a release decision
Define metrics before looking at results. Useful measures include the proportion of material claims supported, citation support rate, contradiction rate, abstention or clarification rate on unanswerable cases, subgroup performance gaps, and the severity-weighted count of failures. Report the denominator and sampling method for every metric. “Accuracy” without the task definition, reference set, and conditions is not reproducible.
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Do not invent a universal pass threshold. A threshold must reflect the consequence of failure, the cost of human review, and the residual risk your organization is willing and authorized to accept. A low-risk drafting assistant may permit a correction workflow; a system that recommends actions affecting people may require near-complete review of material claims and mandatory escalation when evidence is missing.
Use confidence intervals or repeated samples when the test size and design support them, but do not present statistical precision as proof that the test represents deployment. A very large set of unrealistic prompts can be less informative than a smaller, carefully sampled set from the real workflow.
Document results so another team can reproduce them
For each test run, preserve:
- system and component versions, configuration, and test date;
- prompt templates, conversation histories, supplied documents, and tool responses;
- sampling method, number of runs, randomization, and inclusion or exclusion rules;
- ground-truth sources, their versions and dates, and adjudication guidance;
- automated metrics, human-review instructions, reviewer qualifications, and agreement process;
- all material failures, severity, reproducibility, affected groups, and mitigation status;
- untested areas, contamination concerns, privacy constraints, and other limitations.
Keep raw outputs under appropriate access controls, especially when they contain user data. Publish a reader-friendly summary that explains what was tested and what was not, while retaining detailed traces for auditors and engineers.
Repeat after changes and monitor in production
Re-run the full or risk-prioritized suite after a model or routing change, prompt or guardrail edit, retrieval-corpus refresh, tool permission change, interface redesign, or shift in users and operating context. A fix for one failure can create another, so keep the regression corpus and compare results with the prior run under the same scoring rules.
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After launch, sample interactions according to a documented privacy and governance process. Track user corrections, escalations, citation complaints, abstentions, and incidents. Watch for drift in topics, languages, source freshness, and subgroup outcomes. Feed confirmed failures back into the test set. Monitoring is part of evaluation, not a substitute for pre-deployment evidence.
How to tell whether an answer is made up
When reviewing one answer, use a short triage sequence:
- List the answer’s material claims, numbers, names, dates, and causal statements.
- Ask for the source or retrieve the claim independently from an authoritative reference.
- Check that the source exists and actually entails the claim.
- Look for unsupported specificity, invented quotations, impossible chronology, and citations that lead to unrelated material.
- Ask a focused follow-up that tests the same fact from another angle, then compare the result with the original answer.
- If evidence is unavailable or conflicting, require the system to state the limitation or route the case to a person.
This triage can catch an individual error; it does not establish system-wide reliability. Only a documented, representative evaluation can support a deployment claim.
What NIST guidance adds to a test plan
NIST AI 600-1 is a cross-sectoral profile and companion to the AI Risk Management Framework 1.0. Its publication record lists July 26, 2024, as the publication date; the record was updated April 8, 2026. The profile’s measurement guidance supports checking sources and citations, documenting assumptions and limitations, using context-appropriate evaluations, and repeating measurement through the system life cycle.
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The NIST AI Metrology Center catalogs metrics, methods, and tools. NIST explicitly says that inclusion in the catalog is not endorsement, validation, or a suitability determination. Use the listings as leads for designing an evaluation, then verify that a method fits your model, task, population, and deployment conditions.
A defensible release checklist
- The complete system—not just the base model—has been identified and versioned.
- Intended users, tasks, languages, context, and consequences are written down.
- The test set contains routine, ambiguous, unsupported-premise, unanswerable, incomplete, conflicting, and multi-turn cases.
- Expected answers, acceptable uncertainty, and scoring rules were defined before evaluation.
- Material claims were checked against dated ground truth.
- Citations were audited for existence, entailment, and suitability.
- Contradictions and stochastic variation were measured across turns and runs.
- Relevant demographic groups, subgroups, languages, and counterfactual pairs were tested.
- Domain-aware red-teamers probed realistic misuse and failure modes.
- Results include sampling, metrics, human-review procedures, limitations, and severe failures.
- Regression cases and a post-launch monitoring plan are in place.
- The release decision reflects the consequence of missed failures and the human review available to catch them.
The central discipline is to connect every claim about reliability to the evidence that supports it. Confidence, fluency, and a high public benchmark score can coexist with fabricated facts, unsupported citations, contradictions, or unequal outcomes. A documented test suite, expert review, structured red-teaming, and repeat measurement provide a much stronger basis for deciding where an AI system can be trusted, where it needs safeguards, and where a person must remain in control.
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