Ask an AI vendor for evidence you can operate, not a confidence statement you cannot verify. The useful questions connect your intended use, users, data, test conditions, measured results, controls, monitoring, incident response and contractual remedies. Reliability has no universal score: a system that is adequate for drafting internal notes may be unacceptable for a high-impact decision workflow.
Use the questions below in a vendor meeting, request for proposal (RFP), security review and contract negotiation. For every important answer, request five things: the method used, the result, the operating conditions, the person or organization responsible, and the remedy if the result is not achieved.
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Start by defining what “reliable” means for your deployment
Reliability is contextual. Before comparing products, write down the task the AI will perform, who will use it, what inputs it will receive, what happens to its output, and the consequences of an error. Include expected languages, document types, volumes, response times, integrations and periods of peak demand. Identify whether a human must review every output or only exceptions.
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Write the intended purpose and boundaries
- What exact task is in scope, and which user population will operate the system?
- Which uses, decisions, industries, geographies, languages or data types are outside the intended scope?
- What level of error is tolerable for each step, and which errors require a human override?
- What assumptions about input quality, connectivity, integrations and staffing must be true?
Ask the vendor to confirm these boundaries in writing. A system can perform well on a vendor’s demonstration while failing when your users, data quality or workflow differs.
Questions that test reliability before purchase
Ask how the system was evaluated
Request the evaluation design, not just a product tour. Ask:
- Which evaluation datasets and held-out test sets were used?
- Which metrics and evaluation tools were applied, and why are they suitable for this task?
- How closely do the tests represent your languages, edge cases, expected input quality, workload and operating environment?
- Were tests run on the exact model, configuration, retrieval sources and integrations proposed for you?
- Can results be shown by relevant subgroup, scenario, document type or failure severity where that matters?
Request dates, sample sizes, scoring rules, confidence intervals or other uncertainty information when available. Ask whether your team can run a representative acceptance test before signing, and who pays for preparing test data.
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Have the supplier provide the observed results under the stated conditions and a list of known failure modes. Probe for omissions, fabricated content, unsafe recommendations, missed cases, language-specific weaknesses, sensitivity to prompt or formatting changes, latency spikes and integration failures. Ask for examples of both successful and failed outputs, with sensitive information redacted if necessary.
Then agree on an operational threshold for your workflow. Define the metric, the measurement window, the minimum acceptable value, the severity of a breach and who accepts the threshold. Ask: “What happens when the threshold is missed?” Possible remedies include routing to human review, disabling a feature, reverting to a prior version, correcting data, or terminating a service for a material failure.
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Check coverage, scale and technical limits
NIST’s AI Procurement in a Box workbook prompts buyers to examine purpose drift, accuracy and integrity, maintenance, support, SaaS terms and scale limits. Ask the vendor to state:
- Supported data formats, languages, domains and geographic coverage.
- Maximum context, file, transaction, user and concurrent-request volumes.
- Latency targets, rate limits, queue behavior and availability dependencies.
- How performance changes at your expected scale or during peak loads.
- Required preprocessing, retrieval indexes, third-party APIs, connectivity and customer-managed components.
Get each limitation attached to the relevant edition, configuration and service tier. “Enterprise-ready” is not a measurable operating condition.
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Questions about data, access and customer control
Confirm that your data is usable and permitted
Before procurement, establish whether you actually have the data the system needs, in a lawful and usable form. The UK Government Guidelines for AI procurement advise buyers to establish data availability and define whether and how data will be shared during procurement and the later project. Ask:
- What data, labels, metadata, prompts, documents or feedback must we provide?
- Who owns each input and output, and what permissions are required?
- Can the system work with missing, stale, multilingual or low-quality records?
- What validation and transformation must occur before data is sent?
Set use, retention and deletion rules
Obtain a purpose-by-purpose description of how the vendor may use prompts, files, customer records, telemetry and outputs. Ask whether any content is used to train or improve a shared model, whether it is isolated to your tenant, and whether you can opt out. Specify retention periods for live data, backups, logs, prompts, outputs and support tickets; the deletion process; and the evidence you receive when deletion is complete.
Identify every vendor employee, subprocessor and connected service that can access your data. Ask where processing and backups occur, how access is approved and logged, what encryption and key-management options exist, and how privileged access is reviewed. Require notice before a new subprocessor receives your data, with an objection or termination path where appropriate.
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Ask for a live demonstration or configuration document showing whether your administrators can:
- Assign permissions by user, role, task, risk level or data type.
- Restrict features, destinations, tools, connectors, model choices and automated actions.
- Require human approval, escalation or dual control for sensitive outputs.
- Set rate, budget and volume limits and suspend an account quickly.
- View and export prompts, outputs, tool calls, approvals, errors and administrative changes.
Clarify which controls are available to you, which require vendor intervention, and which are impossible because of the architecture. Ask how long logs remain available, whether timestamps and model versions are included, and whether records are tamper-evident enough for investigations.
Questions about the model, suppliers and changes
Map the system and supply chain
Request a current component description: foundation model, fine-tuning or retrieval methods, safety filters, classifiers, orchestration, hosting, data stores and external APIs. Ask which components are supplied by third parties, where they operate, and which party is responsible for security, availability, defects and data handling.
Ask how the model and variables were selected, adapted and evaluated for your proposed task. Request documentation of material limitations and inappropriate uses. The UK guidance specifically points buyers toward questions about model choices, variable selection and system limitations.
Make change management explicit
Ask what counts as a material change: a new model, system prompt, safety policy, training data, retrieval source, feature, hosting region, subprocessor or pricing and rate-limit rule. Establish the notice period, notice channel, information supplied and your right to test or reject a change.
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- Will the vendor provide release notes, version identifiers and evaluation results?
- Can a change invalidate the evidence or threshold agreed for your workflow?
- Must the vendor support a prior version during your re-evaluation period?
- Who owns maintenance, security fixes, model updates and customer communications?
Do not accept a statement that a supplier “follows” a framework as proof that your configured system performs adequately. NIST’s AI RMF 1.0, released January 26, 2023, is voluntary, rights-preserving, non-sector-specific and use-case agnostic. It is a risk-management resource, not a product certification or performance guarantee.
Turn a demo into an operating monitoring plan
A pre-launch demonstration answers what the vendor chose to show on that day. It does not show whether the system will remain suitable after launch. Require a monitoring plan with named owners and access to the measurements.
Define measures, thresholds and drift signals
Agree which quality, safety, fairness, latency, availability, cost and data-integrity measures will be tracked. Document the calculation, sampling method, review frequency, alert threshold and escalation severity. Include outcome measures from your workflow, not only model-internal scores.
Ask how the vendor detects drift in input distributions, user behavior, data coverage, retrieval sources and output quality. Establish how often representative test sets are rerun, how new edge cases are added, and how production incidents feed back into evaluation. Identify who can pause automated actions when a threshold is breached and how service is restored after corrective testing.
Assign ownership across both organizations
Write down a customer owner for business outcomes, a technical owner for configuration and data, and vendor owners for model operations, security and support. The plan should state who reviews dashboards, acknowledges alerts, approves model changes, communicates to affected users and records decisions. OECD’s 2026 due-diligence guidance emphasizes monitoring and tracking performance, documenting test sets and metrics for relevant deployment conditions, and making roles and responsibilities clear.
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Define a reportable incident
Do not limit incidents to a total outage. Include harmful or materially misleading outputs, unauthorized disclosure, prompt or tool abuse, security compromise, loss of logs, failed safeguards, material drift, missed service thresholds and a change that invalidates the agreed evaluation.
Ask how quickly the vendor will notify you for each severity, through which channels and with what initial facts. Name an operational contact and an incident-response contact, including outside-hours coverage when your workflow requires it. Require updates at an agreed cadence until containment and closure.
Agree investigation and remediation duties
- Who can preserve prompts, outputs, model identifiers, configuration, access logs and relevant data?
- What evidence will the vendor provide, and when?
- How will the parties coordinate user, regulator, customer and public communications?
- What corrective action, retesting and root-cause report are required?
- Can you pause a feature, revoke credentials or switch to a safe manual process immediately?
OECD’s guidance calls for communicating incidents to stakeholders and providing or cooperating in remediation. Put those expectations into the operating procedure rather than relying on goodwill during a crisis.
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Make the contract enforce the answers
Translate the due-diligence record into terms that match the use case. At minimum, address:
- Service levels: availability, latency, support response, maintenance windows, credits or other remedies, and exclusions.
- Performance commitments: the metric, test conditions, threshold, measurement rights, cure period and consequence of a material miss.
- Data terms: permitted purposes, retention, deletion, training use, locations, subprocessors, access controls and audit evidence.
- Change control: notice, versioning, re-evaluation, rollback or continued access to a prior version.
- Incident duties: notification times, contacts, cooperation, evidence preservation, remediation and allocation of responsibilities.
- Customer controls: permissions, restrictions, human review, logging, suspension and emergency shutdown.
- Exit and records: export formats, retrieval of data and logs, deletion certification, transition assistance and safe migration deadlines.
Have security, privacy, legal, procurement, compliance and the workflow owner review the same schedule. A service-level credit alone may not compensate for a harmful automated decision; the remedy must fit the impact.
Compare vendors with one evidence request
Send each vendor the same use case, test conditions, questionnaire and evidence deadline. Score the answers only after marking “not provided” separately from “does not meet.” The following axes prevent a headline accuracy claim from dominating the decision:
| Comparison axis | Evidence to request | Decision question |
|---|---|---|
| Contextual performance | Relevant test sets, measured results, failure modes, thresholds, robustness and drift plan | Does evidence cover our users, inputs, workload and consequences? |
| Transparency | Purpose, limitations, dependencies, component map and change notices | Can we explain what the system does and when it should not be used? |
| Customer control | Data-use terms, retention, permissions, restrictions, logs, pause and exit functions | Can we prevent, detect and stop unacceptable behavior? |
| Operational readiness | Support coverage, maintenance, incident notification, escalation and remediation procedures | Can both organizations respond quickly when performance or safety fails? |
| Accountability | Named owners, responsibilities, evidence access and contractual commitments | Is a person empowered to act, and is the obligation enforceable? |
| Fit and proportionality | Safeguards and evidence matched to impact, regulation and business context | Is the control burden appropriate for the harm a failure could cause? |
NIST’s FAQ notes that the relevance and tradeoffs among trustworthiness characteristics vary by situation. Use a weighted score only after your risk owner agrees which axes matter most; do not publish or accept a universal “reliability rating.”
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- A demo is offered instead of test conditions, raw results or failure examples.
- The vendor cannot state intended uses, prohibited uses, data coverage or scale limits.
- “We do not train on your data” is not accompanied by retention, backup, support-access and subprocessor details.
- Model updates can occur without notice, version identifiers, regression testing or rollback.
- There is no named incident contact, evidence-preservation process or customer pause mechanism.
- Logs are unavailable, too short-lived for your investigation needs or omit model and configuration identifiers.
- A framework logo or alignment claim is presented as a certification or guarantee of your deployment’s results.
- Contract remedies cover uptime but not material performance, data-use violations, harmful outputs or transition support.
A practical sequence for your next vendor meeting
- Describe the use case: document users, inputs, outputs, decisions, impact, languages, volume and human-review points.
- Send the evidence request: require evaluation methods, results, limitations, component and data maps, controls and incident procedures.
- Test representative cases: include ordinary, edge, adversarial, low-quality and out-of-scope inputs; record configuration and model version.
- Set acceptance criteria: choose metrics, thresholds, review windows, owners and remedies before production access.
- Run a control and incident exercise: demonstrate permission changes, log export, suspension, notification and restoration.
- Attach the operating plan to the contract: include monitoring, changes, support, data handling, records and exit obligations.
- Review after launch: schedule recurring performance and drift reviews, reassess after material changes and update tests with real failure cases.
The result is a decision record tied to your actual deployment rather than a generic promise. NIST’s voluntary framework organizes suggested actions under Govern, Map, Measure and Manage; its Playbook offers implementation suggestions, not a certification. OECD’s 2026 guidance is risk-based guidance for enterprises across the AI value chain, not universally binding law; check the law and sector rules that apply in your jurisdiction.
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