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The defensible way to test an AI system is to evaluate the complete system in its intended setting—not just the underlying model. Build a documented test set of representative tasks, define the expected answers and acceptable uncertainty, check factual claims against reliable ground truth, verify every citation, probe consistency and counterfactual behavior, and have trained reviewers red-team realistic failures. Record the exact version and conditions, preserve failures for regression tests, and repeat the evaluation after material changes.
What you are actually testing
An AI model score is not evidence that a particular application will be accurate. A product may add a system prompt, retrieval database, tools, filters, conversation memory, and a user interface that change both its capabilities and its failure modes. Test that assembled workflow, including the inputs it receives and the actions users take from its output.
NIST uses the term confabulation for what many readers call hallucination: a generative AI system confidently presents erroneous or false content. The definition also covers an answer that diverges from the prompt or other input, or contradicts something the system said earlier in the same context. See the definition in the NIST Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (AI 600-1, section 2.2).
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That definition makes a fluent paragraph a poor quality signal. A response can be grammatical, detailed, and confidently worded while containing an invented fact, an unsupported inference, a citation that does not exist, or a contradiction hidden several turns earlier.
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Start by defining scope and consequences
Write a short test charter before collecting prompts. It prevents a general benchmark from being mistaken for evidence about a narrow deployment.
Record the system boundary
- Model name, provider or self-hosted build, release identifier, and access method.
- System and developer instructions, temperature or other sampling settings, token limits, safety controls, and output format constraints.
- Retrieval sources, index version, chunking and ranking settings, tool permissions, APIs, calculators, code execution, and conversation-memory behavior.
- User interface steps that transform, truncate, translate, or add context to a user request.
Describe the intended use
State who will use the system, what decisions or tasks it supports, which languages and regions matter, and what information it is allowed to use. A customer-support assistant, a clinical summarizer, and an internal coding helper need different test sets and different tolerances for error.
Classify the impact of failure
For each task, identify the consequence of a wrong, incomplete, biased, or overconfident answer. A harmless brainstorming error may need a warning; an answer that can trigger a payment, deny a service, or influence a health decision needs stronger evidence and human approval. Impact determines the depth of review and whether a single failure blocks release.
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Use real tasks when lawful and safe, or realistic de-identified examples that preserve the same complexity. Keep the provenance of every item so another evaluator can understand why it belongs in the set. Do not rely only on easy questions or a public benchmark that users may never ask.
Include ordinary and difficult prompts
- Common requests that represent the highest-volume use.
- Ambiguous or underspecified questions that should trigger a clarifying question.
- Prompts with an unsupported premise, such as a nonexistent law, paper, product feature, or event.
- Requests beyond the system’s stated knowledge, date range, tool access, or language coverage.
- Incomplete, conflicting, or deliberately noisy source material.
- Multi-turn follow-ups that test whether the system preserves, updates, and corrects earlier facts.
- Instructions that conflict across user, developer, and retrieved content.
- Tasks requiring a refusal, a safe alternative, or an explicit statement of uncertainty.
Write expected answers before running the model
For each item, specify the facts that must appear, facts that must not appear, acceptable alternative wording, required citations or evidence, and conditions under which “insufficient information” is the correct result. If several answers are valid, define the scoring range instead of pretending there is one exact sentence. Include a trusted reference, database record, calculation, or expert adjudication route for every claim you intend to score.
Protect sensitive data
Remove personal identifiers and confidential material unless the deployment is explicitly authorized to process it. Record consent, retention rules, access controls, and whether test prompts could enter provider logs or future training. A test set that creates a privacy incident is not a successful evaluation.
Measure factuality and citation support
Factuality testing asks whether the answer is true under a stated reference and date. It is different from judging style, relevance, or helpfulness.
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Score claims, not just whole answers
Break a response into atomic claims that can be checked independently. Mark each as supported, contradicted, unverifiable from the available evidence, or omitted when the task required it. Track invented entities, dates, quantities, quotations, causal explanations, and calculations separately; an aggregate pass rate can hide a dangerous failure category.
Verify sources and citations
For every citation, check that the source exists, is accessible to the intended user, actually supports the nearby claim, and is an appropriate authority for the question. A real link that merely mentions a topic is not support for a specific number or conclusion. Also test answers that contain no citations: omission of a source can be a failure when the workflow requires traceability.
NIST recommends reviewing sources and citations during pre-deployment measurement and ongoing monitoring. Its profile cautions, “Avoid extrapolating GAI system performance or capabilities from narrow, non-systematic, and anecdotal assessments.” The recommendation and quotation appear in AI 600-1.
Test uncertainty behavior
Give the system questions with no established answer, conflicting references, or a missing input. A high-quality response identifies the gap, asks for what is needed, or gives a bounded answer with an uncertainty statement. Penalize a confident guess even when it happens to match the reference by chance.
Probe consistency across turns and runs
Run the same prompt more than once when sampling is nondeterministic, and compare answers under controlled settings. Then create conversations in which a later question depends on an earlier fact. Look for:
- Contradictions between turns, such as changing a date, definition, or recommendation without acknowledging the change.
- Failure to correct an earlier answer after the user supplies authoritative evidence.
- Loss of constraints when the conversation becomes long or includes large retrieved documents.
- Different answers caused by harmless wording changes, formatting, or language switches.
- Inconsistent tool calls, calculations, or citations for identical inputs.
Save the complete transcript and configuration for every failure. A single answer may look correct in isolation while the conversation as a whole violates the user’s requirements.
Test blind spots, bias, and subgroup effects
Blind spots are systematic omissions or degraded outcomes that may affect particular groups, contexts, languages, or viewpoints. Decide which dimensions matter from the use case rather than applying an arbitrary checklist.
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Choose relevant groups and outcomes
Document the demographic groups, dialects, countries, accessibility needs, professions, and subgroups that could experience different results. Select outcomes that matter: factual accuracy, refusal rate, quality of explanation, ranking, recommendation, escalation, or resource allocation. A generic language-model score may be unsuitable for a system that affects people’s access to services.
Use paired and counterfactual prompts
Create prompt pairs that differ only in a protected or otherwise relevant attribute. Compare factual content, assumptions, tone, safety treatment, and the action recommended. Counterfactual tests can reveal a hidden dependency even when each individual answer appears plausible. Use low-context prompts as well as detailed ones; sparse wording can expose assumptions that a richly described scenario masks.
Interpret differences carefully
A disparity is a signal to investigate, not proof of intent or a complete fairness finding. Check sample size, language quality, task difficulty, and whether the reference answer itself embeds a contested assumption. Document which groups were not tested and whether the data could have appeared in model training. NIST’s profile discusses subgroup evaluation, counterfactual approaches, benchmark suitability, and possible train/test contamination in its measurement guidance: NIST AI 600-1.
Run a structured red-team exercise
Red-teaming is organized probing for realistic failures, not a random collection of clever prompts. The value comes from coverage, context, and analysis.
Assemble the right testers
Include people who understand the deployment, likely users, abuse cases, and relevant domain rules. For high-consequence systems, add subject-matter specialists who can recognize subtle factual, legal, medical, financial, security, or discriminatory errors. Include testers with varied language and cultural backgrounds when those differences are relevant.
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Assign categories such as misleading premises, low-context wording, conflicting instructions, prompt injection through retrieved text, edge cases, privacy leakage, unsafe advice, discriminatory assumptions, tool misuse, and fabricated citations. Ask testers to vary one factor at a time where possible, then combine factors to reproduce realistic conditions.
Analyze, prioritize, and reproduce
For each finding, capture the prompt sequence, system state, output, expected behavior, severity, likelihood, affected users, and a minimal reproduction. Separate a model defect from a retrieval, interface, policy, or operational defect. Prioritize failures by potential harm and ease of triggering, not by how surprising the wording looks. NIST describes red-teaming as structured probing and emphasizes that domain knowledge and deployment awareness influence the usefulness of results in AI 600-1.
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Choose methods by the question they answer
No single score establishes that an AI system is reliable. Select complementary methods and state what each can and cannot show.
| Method | Best question | Evidence produced | Main limitation |
|---|---|---|---|
| Ground-truth claim checking | Is the answer factually correct? | Atomic-claim labels against a trusted reference | Requires current, unambiguous ground truth and may not cover open-ended tasks |
| Citation audit | Does the evidence exist and support the claim? | Source existence, entailment, authority, and accessibility records | A citation can be valid while the uncited parts of an answer remain wrong |
| Curated benchmark | How does the system perform on a repeatable task set? | Comparable scores under recorded conditions | May not resemble deployment, may omit important groups, and may have train/test contamination |
| Structured expert review | Is the response acceptable in a specialized context? | Rubric-based judgments and adjudicated disagreements | Costs more and can vary unless reviewers are calibrated |
| Paired or counterfactual testing | Does changing a relevant attribute change outcomes? | Within-pair differences in quality, assumptions, or decisions | Results depend on the chosen pairs and may not generalize |
| Adversarial red-team probing | How does the system fail under realistic pressure? | Reproducible attack and failure cases | Coverage is difficult to quantify; a clean exercise does not prove safety |
| Production monitoring | What fails after release with real traffic? | User reports, sampled reviews, drift and incident trends | Rare or severe failures may be missed without targeted sampling |
NIST’s Metrology Center lists metrics, methods, and tools, but explicitly says inclusion is not NIST endorsement, validation, or a suitability determination. Treat the listings as leads for designing an evaluation, not as product recommendations: NIST AI Metrology Center.
Use a repeatable scoring and review procedure
- Freeze the test conditions. Record model and application versions, prompts, retrieval corpus, tools, sampling settings, date, locale, and any seed or determinism option.
- Run the fixed set. Keep a protected evaluation split that is not used to tune prompts, and a development split for iteration. Log raw inputs, outputs, tool traces, and retrieved passages.
- Apply automated checks. Use exact matching, calculations, schema validation, citation URL checks, and other deterministic tests where appropriate. Treat an automated judge as a reviewer aid, not unquestionable ground truth.
- Perform blinded human review. Hide model identity when feasible, train reviewers on the rubric, and have a second reviewer adjudicate disagreements or high-severity items.
- Calculate category results. Report factual error, unsupported citation, omission, contradiction, uncertainty, subgroup, safety, and tool-use results separately. Include denominators and confidence intervals or other uncertainty descriptions when your sampling design supports them.
- Inspect failures manually. Read representative passes and every severe failure. Determine whether the cause is model knowledge, retrieval quality, prompt design, guardrails, interface truncation, or reviewer disagreement.
- Set release criteria explicitly. Define which failures are blockers, which require human approval, and which are accepted with monitoring. Do not import a universal pass threshold from an unrelated model, population, or task.
Document results so they remain defensible
A report should let another team reproduce the evaluation and understand its limits. Include:
- System description and intended users, tasks, languages, regions, and impact classification.
- Model, application, prompt, retrieval, tool, guardrail, and UI versions.
- Test-set size, provenance, sampling method, inclusion and exclusion rules, and sensitive-data handling.
- Expected-answer sources, scoring rubric, automated checks, reviewer qualifications, calibration, and adjudication process.
- Results by task, failure type, subgroup, language, severity, and confidence or uncertainty measure where applicable.
- Representative failures with complete reproduction steps and the mitigation or decision taken.
- Known blind spots, possible contamination, unsupported generalizations, and conditions not tested.
NIST’s Generative AI Profile is a cross-sector companion to AI RMF 1.0. It was published July 26, 2024; NIST’s publication record was updated April 8, 2026 (publication record). Use the document as risk-management guidance, not as proof that any particular test suite or tool is suitable for your system.
Turn failures into regression tests and monitoring
Preserve each confirmed failure, its minimal prompt, expected behavior, severity, and the change intended to fix it. Add it to a regression set before altering the model or prompt. Re-run the full protected set after changes to the model, system instructions, retrieval corpus, embedding or ranking method, tools, guardrails, user interface, or deployment context.
After release, sample interactions according to risk, route user reports into triage, and watch for drift in topics, languages, sources, and failure categories. Monitor citation validity and retrieval freshness when answers depend on changing information. A benchmark run is a snapshot; production evidence is a separate stream that can reveal failures the original set never represented.
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Common mistakes and how to correct them
“The answer sounds confident, so it is probably right.”
Replace fluency judgments with claim-level checks against a named reference and an uncertainty rubric.
“A high public benchmark score proves our product is ready.”
Run tasks from your users with your retrieval, tools, prompts, and policies. State exactly how the benchmark differs from deployment.
“We tested a few famous trick prompts.”
Use a sampled, documented set with ordinary traffic, edge cases, multi-turn contexts, subgroup coverage, and realistic abuse paths. Keep the trick prompts as one category, not the whole evaluation.
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“One automated judge can score everything.”
Combine deterministic checks, expert review, and adjudication. Validate the judge on known examples and inspect disagreements.
“No disparity appeared in our aggregate score.”
Break results down by relevant groups, languages, and subgroups; use paired prompts and outcome measures that reflect the actual decision or service.
“We fixed the prompt once, so testing is finished.”
Re-run after every material system or context change and continue monitoring real use. A mitigation can remove one failure while introducing another.
Quick Recap
A practical release checklist
- ☐ The complete application, not only the base model, is identified and versioned.
- ☐ Intended users, tasks, languages, contexts, and consequences of error are documented.
- ☐ The test set contains common, ambiguous, unsupported-premise, out-of-scope, incomplete, conflicting, and multi-turn cases.
- ☐ Expected answers, acceptable uncertainty, references, and scoring rules were written before evaluation.
- ☐ Claims, citations, omissions, contradictions, and uncertainty behavior were checked separately.
- ☐ Relevant demographic groups, languages, subgroups, and counterfactual pairs were tested.
- ☐ Domain-informed red-teamers attempted realistic misuse and edge cases.
- ☐ Automated results were supplemented by blinded human review and adjudication.
- ☐ Results include limitations, possible contamination, untested conditions, and representative failures.
- ☐ Severe failures have owners, mitigations, regression cases, and explicit release decisions.
- ☐ A post-deployment monitoring and re-test trigger is defined.
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