Commit to tagging a small transcript set and turning the pattern into one design hypothesis.
Run a 20-transcript repair scan for a conversational assistant. Use fallback logs, no-match transcripts, user-test recordings, or support escalations tied to a bot conversation. Transcript source: 20-item sample rule: Tags I will use: Largest pattern found: Three Whys behind it: Design hypothesis: Follow-up signal in 3 days: Twenty no-match turns from a customer-support bot where users ask about wrong charges, refunds, or invoices. Twenty enrollment-assistant transcripts where users abandon after a required-document question. Twenty scheduling-bot repair turns where users say "move it," "cancel that," or "same time next week." Twenty HR document-bot fallbacks around vague phrases like "proof," "letter," "certificate,"…
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