How to test AI answers before your team relies on them

A product demo answers five questions beautifully. Then a customer asks about an exception buried in the returns policy, and the assistant invents a confident answer. How would your team catch that before the answer reaches someone?

You don’t need a research lab to start testing. You do need examples drawn from the work, a way to judge the answers, and someone with enough context to recognize a mistake. A small test set won’t prove a tool is safe for every situation, but it can tell you whether a proposed workflow deserves a closer look.

Start with one job

Choose a specific task, such as drafting replies to common support questions using an approved policy. Decide what the assistant may use and what a person must do before anything goes to a customer. For an early test, keep sending in human hands.

Write down the result you want. A useful support draft answers the question using the current policy, includes any relevant conditions, and admits when the policy doesn’t provide an answer. A polished paragraph that promises a refund the policy doesn’t allow has failed, even if it sounds helpful.

Collect examples that resemble the work

Ask the people who handle the task to help choose a set of questions. Include routine requests, awkward wording, exceptions, and cases with missing or conflicting information. For each question, record the relevant approved source and what a good answer should include. Add a few cases where the right response is to ask a follow-up question or hand the issue to a person.

Use only information approved for the tool and account. If customer records cannot be used, create synthetic or appropriately redacted examples. Note that these may miss the messiness of actual work. Keep your test cases and answers in a location appropriate for their sensitivity.

For a first pass, a team might assemble 20 to 30 cases. That number is a practical starting point, not a statistical guarantee. Record how the cases were selected and what they leave out; a handful of easy examples can make a weak assistant look dependable.

Agree on what counts as a pass

Before looking at the results, write a short review guide. For each answer, a reviewer can check whether it:

• States the correct policy and includes conditions that change the outcome.
• Avoids adding unsupported promises or details.
• When information is missing, acknowledges the gap and asks a follow-up question or routes the case appropriately.
• Gives a customer a clear next step.

Flag serious errors separately from small wording problems. A wrong refund promise and an awkward sentence should not receive the same weight. Keep the assistant’s original answer alongside the reviewer’s corrections, so a good final reply does not hide an unreliable draft.

Ask two knowledgeable reviewers to independently score some of the same cases, then discuss disagreements and improve the guide. Human review takes judgment too. If reviewers interpret the policy differently, the policy or the rubric may need work before anyone can assess the assistant fairly.

Run the test and inspect the failures

Use the same instructions, source material, and settings your team plans to use. Save the tool or model version when available, the date, the inputs, the outputs, and the review decisions. Compare results with the current process using comparable cases and the same quality criteria. Include the time spent checking and correcting drafts.

Look at individual failures before celebrating an average. Suppose 26 of 30 answers pass, but the four failures all concern a particular exception. That is a reason to investigate the exception and consider limiting the workflow, not a reason to declare the assistant 87% reliable. A small, selected set cannot establish its error rate in general use.

The NIST AI Risk Management Framework recommends pairing accuracy measurements with clearly defined test sets that represent expected conditions of use. Its generative AI profile also discusses how testing may miss impacts that emerge in real-world conditions. Those are good reasons to keep checking after a pilot starts.

Turn the results into a decision

Review the findings with the people who will use and oversee the tool. If answers are often wrong, improve the source material or narrow the task and test again. If errors are difficult to spot or the potential harm is too high, stop. If the results support a limited trial, assign an owner, make review time part of the workflow, and decide how concerns reach that owner.

Repeat relevant tests when the policy, instructions, model, or workflow changes. During use, review an appropriate sample of cases and track serious incidents. A test set should grow when the team finds a new failure, but passing old examples alone won’t establish that new situations are covered.

If your team would like help designing a practical evaluation or training reviewers, explore our AI strategy and AI training services.