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  "name": "How do I test an AI support workflow before letting it talk to real customers?",
  "description": "Create a test set from real tickets plus edge cases, incomplete requests, contradictions, complaints, and exceptions. Check factual accuracy, source use, classi",
  "url": "https://runmyaibusiness.com/faq/how-do-i-test-an-ai-support-workflow-before-letting-it-talk-to-real-customers",
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        "text": "Build a small test set from real past tickets, including normal questions, incomplete requests, contradictory information, angry messages, and cases that required exceptions. Compare the assistant’s answer, evidence, classification, and handoff against the outcome a trained human would expect. Context: Testing history before launch is a practical way to expose gaps that a polished demonstration can hide. Run My AI Biz’s materials also distinguish demonstrated workflows from experimental or not-yet-used examples, so testing should precede claims about performance. Key factors: - coverage of common categories - edge cases - policy changes - false confidence - escalation quality. Practical guidance: Keep a repeatable evaluation file and rerun it after prompt, policy, model, or integration changes. Do not use customer-facing automation until failures have owners and corrective actions.",
        "description": "Create a test set from real tickets plus edge cases, incomplete requests, contradictions, complaints, and exceptions. Check factual accuracy, source use, classification, escalation, and proposed actions. Rerun the set whenever prompts, policies, models, or integrations change."
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