Customer Experience & Conversational AI Practical insights
Test whether Fin asks useful clarification questions
Evaluate Fin’s clarification questions against realistic customer tasks, separating necessary follow-ups from repeated questions and avoidable friction.
A clarification question earns its place when the answer changes what the assistant should do. Fin documents occasional follow-up questions within its conversational experience. [1] To assess that behavior in your own setup, test complete customer tasks rather than deciding whether individual messages sound friendly.
Build ambiguous and complete cases
Create paired scenarios. In one, a customer asks why an export failed without naming the file type. In another, the same customer already provides the file type and error message. Define the minimum detail needed for a reliable response. The first case may justify a question; the second should not repeat information already supplied.
Write an expected decision
For each scenario, record the next appropriate move: answer, clarify, check account information or hand off. Do not prescribe one exact sentence. Several phrasings may work if they seek the same necessary fact. Include cases where the customer cannot supply a requested detail, so the test covers recovery rather than a perfect scripted exchange.
Review the whole exchange
Count repeated questions and unnecessary turns, but also inspect correctness. A shorter conversation is worse if the assistant guesses the wrong product version. Ask a reviewer whether each clarification changed the available action. Record when a customer would reasonably abandon the process, such as a third request for information already included in the opening message.
Change one cause at a time
If Fin asks about a detail absent from the knowledge base, repair the content before repeatedly changing tone guidance. If it overlooks supplied information, investigate the conversation configuration and test again. Keep product settings and test cases recorded so comparisons are meaningful. These are proposed evaluation steps, not results from an Markaigen deployment. Use the findings to improve successful task completion, with conversational brevity as a supporting measure.
Sources and evidence
Sources checked on 4 October 2026. Proposed workflows and hypothetical examples are editorial analysis.
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