Build a Voice Assistant Regression Call Set That Catches Real Regressions

A voice assistant regression test should tell you whether a change broke a business behaviour that previously worked. Repeating a friendly demonstration is a weak substitute: it exercises the route you already know, while real callers change details, hesitate and ask questions in an unexpected order.

Start with a small, named collection of conversations whose expected outcomes a person can judge. The collection becomes a shared agreement between the business owner, the person configuring the assistant and anyone reviewing a release. It can live in a simple document; a dedicated evaluation dashboard is optional.

Choose behaviours before writing dialogue

List the assistant's responsibilities and the actions it must avoid. A hypothetical repair business might allow service-area questions, issue summaries and callback collection. It might prohibit firm price promises and same-day guarantees. Each responsibility needs at least one test, and each prohibition needs a caller who asks for it.

Write expectations in terms of results. “Politely handles a difficult caller” is too vague. “Explains that a price cannot be confirmed, records the caller's request and does not invent a quote” gives a reviewer something observable.

Use Burki's voice testing guide for the wider testing process, then make this collection specific to the workflow you actually intend to launch.

Give every case a compact record

For each conversation, capture:

• A stable case name and the business rule it exercises.
• Starting facts, including any intentionally missing information.
• The caller's objective, without forcing an exact script.
• Expected final facts and permitted actions.
• Actions or statements that would fail the case.
• The evidence required to decide the result.

For example, callback-number-correction might start with one telephone number and then replace its final digits. Success requires the corrected number in the final saved record. A fluent spoken readback with the original number still stored is a failure.

Keep personal data fictional. If a real incident motivates a case, rewrite identifying details while preserving the conversational difficulty. The original incident can remain in its appropriately restricted location.

Include ordinary and awkward conversations

A useful first collection covers a clean request, a correction, an ambiguous request, an unsupported request, a caller interruption and an unavailable dependency. Add a return to an earlier topic: callers often supply a missing detail after the assistant has moved on.

Avoid making every test adversarial. If the ordinary enquiry becomes slower or more confusing after a safety improvement, that is also a regression. Balance the collection so common tasks and expensive mistakes both receive attention.

Keep a few cases outside the routine editing loop. These provide a check against repeatedly tuning the assistant to the same familiar wording. They do not make the evaluation statistically representative, but they reduce the temptation to optimise only for rehearsed examples.

Match the test method to the claim

Text-level tests can check tool arguments and response behaviour; LiveKit documents turn-level assertions in its unit testing guide. Audio cases are needed for interrupted speech, pauses, pronunciation and listening accuracy. A telephone route adds further requirements that a browser conversation does not exercise.

Do not label a mocked action as provider acceptance. Record whether the evidence came from a simulated result, a real provider response or a separately inspected final record. Those are useful layers with different limits.

Compare versions without hiding failures

Run the same case collection against the previous and proposed configuration. Keep the prompt, model, knowledge revision and integration permissions with each result. Record failures individually instead of reducing everything to a single pass percentage.

When a test is ambiguous, fix its expectation before using it to approve the release. When the product is wrong, preserve the failing example and change the implementation. Editing the expected answer solely to turn a red result green defeats the purpose.

After release, add new cases from meaningful incidents and retire redundant ones with a reason. A compact set that people trust is more useful than hundreds of unchecked transcripts. Start by selecting five behaviours your team refuses to break, then follow the reviewed-change approach in Burki's production-safe learning guide.