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Before shipping an AI application—or changing its model, prompt, retrieval, tools, or safeguards—run a repeatable set of tests against the full deployed configuration. The 20 cases below are a practical checklist, not an official NIST standard or a universal release threshold. Adapt them to your users and risks, and define in advance which failures block release.
What should an AI smoke evaluation cover?
A smoke evaluation is a compact, repeatable check for high-impact failures in a particular build. It is not a substitute for broader evaluation. There is no context-free metric set that can establish readiness: the intended use and deployment context affect how accuracy, interpretability, privacy, reliability, robustness, safety, security, and harmful bias should be measured. NIST’s measurement guidance treats these as distinct characteristics, not one score: NIST AI measurement and evaluation.
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NIST’s ARIA approach combines model testing, red-teaming, and user testing; each reveals a different kind of evidence. A prompt-only test can miss failures in the application around the model. For agents in particular, performance depends on the environment and setup as well as the model. Assess the workflow, tools, and safeguards users will actually encounter, not just a stripped-down prompt. See NIST’s ARIA manual and OpenAI’s guidance for third-party evaluations.
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NIST summarizes why rigor matters: “The development and utility of trustworthy AI products and services depends heavily on reliable measurements and evaluations of underlying technologies and their use.”
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The 20 cases to run before release
For each case, write down the input or setup, expected behavior, scoring rule, severity, owner, and release consequence. Make the example specific to your product; the prompts below describe test intent rather than universal test data.
Core behavior and quality
- Golden-path task. Give the system a common, intended task using a representative input. Check that it completes the task correctly and returns the promised result.
- Grounding and citation. If the product promises sourced answers, ask a question answerable from supplied or retrieved material. Verify that material supports the claims and that citations point to the right evidence.
- Unknown or missing evidence. Ask for information that the available evidence does not establish. The expected behavior is to identify the gap, not fill it with an invented answer.
- Instruction and format adherence. Provide relevant constraints and a required output structure. Check both substantive compliance and whether the output is parseable where the application requires it.
- Regression on known failures. Rerun high-impact examples that previously exposed defects. Confirm that the change has not reintroduced a failure that was fixed.
Robustness and safety
- Ambiguous request. Use a request with two plausible meanings where choosing incorrectly matters. Check that the system asks a useful clarifying question or takes a conservative path.
- Adversarial phrasing. Rephrase or obfuscate a request that should trigger a safeguard. Check whether the safeguard still works rather than relying on one familiar wording.
- Unsafe request. Test a request that violates the product’s defined safety policy. Confirm the system refuses or redirects as that policy requires.
- Sensitive information. Attempt to elicit secrets or personal information outside the authorized purpose. Check that the response does not disclose it.
- Bias-sensitive case. Compare relevant user-group cases in the product’s context. Look for materially different harmful treatment, using a comparison appropriate to the task rather than assuming one generic fairness score is sufficient.
Data and retrieval
- Stale or conflicting source. Supply sources with an old date or conflicting claims. Check that the answer surfaces the conflict or date limitation instead of presenting stale information as current.
- Retrieval miss. Make retrieval return no useful result. The system should not behave as if relevant evidence was found; check for a safe fallback or a clear statement of the limitation.
- Prompt injection in supplied content. Put instructions that conflict with the system’s rules inside retrieved or uploaded content. Check that the application treats that text as data rather than authority.
- Data boundary. Test whether information from one user, tenant, or session can affect another’s answer. Verify isolation at the application boundary, not only in a model response.
- Input edge case. Try empty, malformed, unusually long, or unsupported input. Confirm behavior stays within declared limits and fails safely when it cannot proceed.
Tools, permissions, and operations
- Tool selection. Give a task for which a tool is needed and one for which it is not. Check that the system selects the appropriate tool—or refrains when tool use is unnecessary.
- Tool arguments. Inspect the arguments sent to a tool. They should be valid, constrained, and consistent with the user’s intent.
- Authorization and consequential action. Test an irreversible or high-impact action. Confirm the system obtains the intended approval before taking it.
- Tool failure and retry. Simulate a timeout or error. Check that the system fails safely or recovers without duplicate or uncontrolled side effects.
- Latency, cost, and fallback. Run representative requests under the product’s stated operational budget, then make a dependency unavailable. Check that the service stays within its limits or degrades safely.
How to decide what blocks a deploy
Set release consequences according to the product’s use and risk; no universal threshold follows from this checklist. A critical privacy leak, unauthorized consequential action, or severe safety failure may warrant a hard stop. A low-severity formatting regression may instead require triage, depending on whether it breaks a real user workflow. Record the team’s policy before evaluating a build so the result is not decided ad hoc.
- Hard block: Define failures that create unacceptable safety, privacy, security, authorization, or data-isolation risk.
- Review before release: Identify failures whose significance depends on affected users, frequency, or available workarounds.
- Track without blocking: Record lower-severity defects that do not cross the team’s release threshold, with an owner and follow-up plan.
For every run, preserve the target build and configuration; test data and whether it is public, held out, or rotated; run conditions and retries; expected behavior and scoring; severity; owner; and release consequence. OpenAI’s third-party evaluation guidance emphasizes describing the evaluation claim and content, tested system, budget, elicitation method, and validity checks so decision makers can interpret results. Its guidance also names reward hacking, evaluation awareness, contamination, refusals, and sandbagging as behaviors that can undermine interpretation.
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Maintain a small stable regression set for routine changes, including known high-impact failures. Also keep some cases blind or rotate them. A test set that becomes familiar can invite overfitting or contamination and stop representing a meaningful check. NIST describes AITE’s use of blind data in a sequestered environment to mitigate train/test contamination: NIST AITE overview. OpenAI’s evaluation guidance likewise calls for validity checks that consider contamination and evaluation awareness.
Do not treat a pass on these 20 cases as proof that a system is safe or ready for every use. They are a small operational screen, and their value depends on how closely they match the product’s actual users, data, model, tools, and safeguards.
Which evaluation mode answers which question?
| Mode | Question it helps answer | Useful consideration |
|---|---|---|
| Model testing | How does the model behave on defined tasks and cases? | Useful for component behavior, but does not alone establish how the complete application performs. |
| Red-teaming | How can adversarial inputs expose weaknesses? | Use scenarios matched to the system’s risks; a small smoke set cannot cover every attack. |
| User testing | How does the system work for people in use? | Helps reveal experience and workflow failures that a model-only check may miss. |
These modes are complementary, not competing substitutes. Choose based on the question, fidelity to deployment, time and cost, relevant risk coverage, and whether cases are public or held out. NIST’s TEVV-Athlon describes an assessment framework customizable to organizational objectives: NIST TEVV-Athlon.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why this is a checklist, not a universal benchmark
NIST’s AI measurement and evaluation page reports that it has designed and conducted hundreds of evaluations of thousands of AI systems; the page does not give a publication year for that statement. Separately, the MLCommons paper describing AI Safety Benchmark v0.5 (2024) reports 13 hazard categories, tests for seven categories, and 43,090 templated test items. That figure describes one benchmark, not a recommended number of smoke tests. Neither statistic establishes a universal 20-case list or release gate.
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