How to Turn Support Tickets Into AI Test Scenarios

Export recent tickets, chats, and email threads from the queue the AI will touch first. Keep the customer's original phrasing because missing context, angry tone, typos, and bundled requests are what break support AI in production.

Group the tickets into specific intents. Billing is too broad. A customer asking why an annual renewal charged after cancellation is specific enough to source, test, approve, restrict, or block.

Every scenario needs a current source: help article, macro, SOP, policy, product page, security document, or approved answer. If no source exists, the correct test result is blocked, not model failure.

This is where Meihaku differs from pure runtime testing. The test scenario is not only a prompt. It is a prompt plus the evidence that makes an answer defensible.

A failed scenario should create one of four actions: fix the source, restrict the intent, block the intent, or keep it human-owned. After the source changes, rerun the same customer phrasing.

That creates a production-to-test flywheel, but it starts earlier: the root cause may be the support knowledge, not the model.