Before launch, test a customer service chatbot against realistic customer questions and documented expected outcomes, then check its knowledge, security, privacy, integrations, accessibility, usability, and behavior when something goes wrong. Combine controlled tests with adversarial testing and sessions with real users. Record failures, fix them, and rerun affected cases against the actual deployed configuration and knowledge sources; a successful lab test alone does not establish that the production chatbot is ready.
What a pre-launch chatbot test should cover
A chatbot can produce a plausible answer and still fail the customer: it may rely on an unapproved source, expose information, mis-handle an order lookup, or make it difficult to reach a person. Evaluate the whole service, not just the model’s ability to answer sample questions.
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| Evaluation approach | What to test | What it can reveal |
|---|---|---|
| Model and expected-case testing | Known customer intents, approved answers, required actions, and handoff conditions | Incorrect, incomplete, inconsistent, or unsupported answers under controlled conditions |
| Red teaming | Adversarial prompts, attempts to bypass instructions or access controls, and other misuse relevant to the system | Security weaknesses, unsafe responses, data exposure, and failure modes that ordinary questions may not reveal |
| User testing | People attempting representative tasks, including users with differing needs and assistive technology where applicable | Confusing conversation flows, inaccessible controls, unclear errors, and barriers to completing a task or reaching a human |
NIST’s ARIA Evaluation Planning Manual, published September 18, 2026, describes holistic AI evaluation as combining model testing, red teaming, and user testing. These approaches answer different questions; none replaces the others.
1. Define what the chatbot is allowed to do
Before drafting test cases, write down the chatbot’s intended customer-service tasks and boundaries. A useful scope document makes it possible to judge a response against a concrete expectation rather than a reviewer’s general impression.
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- Tasks it should handle: for example, explain a return policy, find an order after appropriate authentication, or collect details for a support case.
- Tasks that require a human: specify the triggers, the destination, and what information the bot should pass along.
- Requests it should decline or redirect: define what to do when a request falls outside its role or cannot be answered from approved information.
- High-impact or high-volume cases: document the correct answer or action and the escalation path for each.
Set acceptance criteria before reviewing the results. Criteria should reflect the consequences of an error: an incorrect greeting and an unauthorized account disclosure are not equivalent failures. There is no universal pass percentage or checklist threshold established for every customer-service chatbot; the organization must set release criteria for its use case and risk.
2. Build a test set from real customer needs
Use customer questions and support scenarios that reflect the language people actually use, provided you are permitted to use those examples and handle any personal information appropriately. Group cases by intent and expected outcome. Each case should state what the bot should answer, do, refuse, or hand off, so different reviewers can assess it consistently.
Include more than the ideal phrasing
- Common questions and frequent support intents.
- Paraphrases, informal wording, misspellings, and short or incomplete messages.
- Ambiguous requests and multi-part questions.
- Questions outside the chatbot’s scope, including cases where it should say it cannot help and provide a next step.
- Scenarios that require an action, verification, or human handoff rather than a written answer.
In its 2025 NCCoE chatbot study, NIST manually selected and revised about 100 questions with corresponding ground-truth answers. NIST also reported that question-and-answer pairs generated by an LLM often lacked sufficient specificity. That is an example from one study, not a recommended minimum size for every chatbot’s test set. Human review matters: test cases need enough detail to distinguish a correct response from a merely plausible one. See NIST IR 8579: Developing the NCCoE Chatbot, an initial public draft dated July 31, 2025, documenting a particular internal-use prototype rather than universal implementation guidance.
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3. Test answers against approved knowledge
For each case, check whether the answer is accurate, relevant to the customer’s task, and grounded in the knowledge the business has approved. Try the same intent in different wording to see whether the chatbot remains consistent, and test whether it admits uncertainty instead of guessing when the available information does not support an answer.
If the chatbot uses retrieval-augmented generation
Check the retrieval step as well as the generated response. Test questions whose answer is present in the source material, questions for which no supporting source exists, and cases where sources are incomplete or conflict. A chatbot that retrieves the wrong passage can confidently give a wrong answer even if the underlying documents contain the right information.
NIST IR 8579 discusses a specific RAG chatbot implementation and its security and evaluation considerations. Treat it as a case study, not as proof that every RAG system behaves the same way.
4. Probe security, privacy, and failure behavior
Test risks that follow from the chatbot’s design, data, permissions, and connected services. NIST’s chatbot report discusses prompt injection, hallucinations, data exposure, and unauthorized access. These are reasons to include security cases in a customer-service evaluation, not a claim that every chatbot has the same vulnerabilities.
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- Prompt injection and instruction bypass: try inputs that ask the bot to ignore its rules, reveal internal instructions, or use a tool for an unauthorized purpose.
- Customer-data isolation: verify that one user cannot obtain another customer’s order, account, or case details, and that authentication is required before protected information or actions are exposed.
- Internal information: check that private operational material, credentials, or data not intended for customers cannot be retrieved or disclosed.
- Unsupported claims: test questions with no approved answer and confirm that the bot does not fabricate policies, account details, or assurances.
- Unavailable dependencies: simulate a failed or delayed API, retrieval source, or downstream service and check that the bot reports the problem honestly and gives a safe next step.
NIST’s prototype report documents mitigations including access controls and validation filters. Those details are implementation-specific; use the report to understand the risks it examined, not as a drop-in security checklist for a different deployment.
5. Exercise integrations, actions, and handoffs end to end
When the chatbot is connected to support systems, test the real deployed path rather than only a demonstration or mocked response. A correct-sounding message does not prove that an account lookup, ticket, or transfer actually completed.
- For account lookup, verify authentication, the returned information, and what happens when credentials or account details are invalid.
- For order or case status, compare the response with the connected system and test missing, stale, or unavailable records.
- For ticket creation, confirm the ticket is created with the right customer details and context; check duplicate submissions and interruptions.
- For human handoff, confirm that the transfer reaches the intended destination, preserves useful conversation context, and gives the customer a clear message if no agent is available.
- For delayed, failed, interrupted, or repeated actions, verify that the bot does not claim success when the connected service did not complete the action.
Record the result of the action in the test case, not just the text shown in chat. The relevant expected outcome might be “case created and reference supplied,” “authentication requested,” or “handoff initiated,” rather than a particular wording alone.
6. Test accessibility and usability with intended users
Check whether people can operate the chatbot and finish a support task, not merely whether it opens. MITRE’s Chatbot Accessibility Playbook recommends testing functionality, performance, security, usability, and accessibility, and involving diverse target users. Section508.gov likewise recommends systematic accessibility testing and usability testing with people with disabilities and assistive technology.
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- Navigate the chat interface by keyboard and check the focus order and visibility of focus.
- Use a screen reader to check whether new messages, prompts, errors, and controls are announced understandably.
- Check that errors explain what happened and what the customer can do next.
- Confirm that a user can find and activate the human-support option without relying on a mouse or understanding an obscure command.
- Observe representative users attempting common support tasks; note where they hesitate, misunderstand a response, or cannot recover from an error.
Automated accessibility checks can help identify some issues, but they do not replace manual checks or user testing. Section508.gov describes both automated and manual approaches and cautions about automated-test limitations in its Buy Accessible Products and Services guidance.
For applicable U.S. federal information and communication technology, Section508.gov identifies WCAG 2.0 Level A and AA criteria under the Revised Section 508 Standards. That federal context should not be treated as a universal legal requirement for every private deployment or jurisdiction; legal applicability depends on the organization and service.
7. Run the three complementary evaluation passes
Organize testing so that expected-case checks, adversarial probing, and observation of real users each have a defined purpose. NIST’s ARIA manual provides an evaluation-planning framework, not a customer-service-specific test suite.
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- Model and expected-case pass: run the documented questions and compare each result with its expected answer, action, or handoff.
- Red-team pass: try plausible misuse and adversarial inputs tied to the chatbot’s access, data, tools, and customer-facing role.
- User pass: ask intended users to complete representative tasks while observing comprehension, task completion, accessibility barriers, and recovery from errors.
One useful way to distinguish the methods is by the evidence they produce: controlled cases show what happened for known scenarios, red teaming explores how the system may fail under pressure, and user sessions reveal problems that a technically correct answer may still cause in practice.
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8. Track failures, fix them, and retest
Keep a test record that lets the team see what was tried, what happened, and whether a fix changed the outcome. A practical record can include:
- Test case ID, intent, input, and expected answer or action.
- Observed response and, for integrations, whether the downstream action actually succeeded.
- Failure severity, affected users or workflows, and an owner for the fix.
- Resolution, retest result, and the relevant chatbot configuration or knowledge-source version.
After changing prompts, a model, an integration, access controls, or knowledge sources, rerun affected cases and any connected tests that could be altered by the change. Before release, review unresolved failures against the organization’s pre-set acceptance criteria and risk. Testing the deployed configuration matters because behavior can change when the production instructions, permissions, integrations, or content differ from the test setup.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose an evaluation approach
Whether testing is done in-house or with outside help, compare what the evaluation actually covers and what evidence it produces. A narrow assessment should not be mistaken for an end-to-end readiness review.
- Coverage: Does it include expected-answer checks, red-team cases, user sessions, accessibility, and integration behavior relevant to the chatbot?
- Evidence quality: Are cases human-reviewed, tied to explicit expected outcomes and traceable approved sources, and recorded so results can be reproduced?
- Risk scope: Does the assessment cover the risks you need examined, or only a limited category such as bias?
- User representation: Does testing reflect varied ways of phrasing requests and users with different needs, including assistive-technology users where relevant?
- Repeatability: Can the same cases be rerun after changes so the team can determine whether a failure was fixed or reintroduced?
For example, the GOV.UK listing for FairNow conversational AI and chatbot bias assessment describes a bias and robustness evaluation and explicitly says it is not designed to test safety or security. A scoped bias assessment can inform that part of a review; it is not a substitute for security, privacy, accessibility, integration, or user testing.
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How many questions should be in a chatbot test set?
There is no universal number established for all customer-service chatbots. NIST’s example of about 100 manually selected questions describes one study, not a minimum. Choose enough cases to cover the chatbot’s actual intents, variations, boundaries, and risks, and make the expected outcome specific for each.
Is an automated test result enough to approve a chatbot?
No. Automated checks can support evaluation, but they cannot by themselves establish that a chatbot is secure, usable, accessible, or effective for real customers. Combine repeatable test cases with adversarial testing and user evaluation.
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Does passing a bias assessment mean the chatbot is safe and secure?
No. An assessment has only the scope it claims to cover. The GOV.UK description of FairNow’s method, for instance, says it is not designed to test safety or security.
When should the team rerun tests?
Rerun affected cases after changes to the model, prompts, connected services, permissions, or knowledge sources, and check linked workflows where a change could have side effects. Keep the results associated with the configuration that was tested.
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How many questions should be in a chatbot test set?
There is no universal number established for all customer-service chatbots. NIST’s example of about 100 manually selected questions describes one study, not a minimum. Choose enough cases to cover the chatbot’s actual intents, variations, boundaries, and risks, and make the expected outcome specific for each.
Is an automated test result enough to approve a chatbot?
No. Automated checks can support evaluation, but they cannot by themselves establish that a chatbot is secure, usable, accessible, or effective for real customers. Combine repeatable test cases with adversarial testing and user evaluation.
Does passing a bias assessment mean the chatbot is safe and secure?
No. An assessment has only the scope it claims to cover. The GOV.UK description of FairNow’s method, for instance, says it is not designed to test safety or security.
When should the team rerun tests?
Rerun affected cases after changes to the model, prompts, connected services, permissions, or knowledge sources, and check linked workflows where a change could have side effects. Keep the results associated with the configuration that was tested.
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