Sort inbound tickets into priority lanes with a routing rulebook
prompt.txt
Act as a practical specialist helping me with support triage autopilot. My context: [your support channels; ticket volume per day; top 5 request types; team size and roles; current first-response time; escalation rules].
1. Define 4 priority lanes with one-sentence entry criteria each.
2. Write a classification prompt that tags each ticket with lane, sentiment, and a one-line summary.
3. Draft auto-replies for the two most common lanes with placeholders for [customer name] and [issue].
4. Set escalation triggers that pull a human in within [X minutes].
5. Add a weekly report template counting lane volume and misroutes.
Use my details, not a generic example. If a fact needed for the plan is missing, ask for it or mark the assumption. Keep the result ready to use.
#ai workflows#support automation
💡 Replace the [brackets] with your details — the more specific, the sharper the answer.
Sort a crowded inbox without losing the messages that matter.
Act as a practical specialist helping me with inbox triage blueprint. My context: [mail volume, categories, urgent senders, tools available].
1. Define priority tiers and exact inclusion rules.
2. Write example labels and false-positive cases.
3. Design a daily review queue and escalation path.
4. Give a five-message test set with expected labels..
Use my details, not a generic example. If a fact needed for the plan is missing, ask for it or mark the assumption. Keep the result ready to use.
Turn a broad question into a source-checked investigation.
Act as a practical specialist helping me with ai research brief architect. My context: [question, decision it supports, deadline, sources allowed].
1. Break the question into falsifiable subquestions.
2. Set a source hierarchy and recency requirements.
3. Create an evidence table with claim, source, date, confidence.
4. Specify when to stop researching and how to flag gaps..
Use my details, not a generic example. If a fact needed for the plan is missing, ask for it or mark the assumption. Keep the result ready to use.
Make messy documents yield clean, auditable fields.
Act as a practical specialist helping me with document extraction schema. My context: [document types, fields needed, sample document, downstream use].
1. Design a JSON schema with field types and null handling.
2. Write extraction rules with exact quote or page evidence.
3. List ambiguous cases and a human-review threshold.
4. Produce three test cases including a missing field..
Use my details, not a generic example. If a fact needed for the plan is missing, ask for it or mark the assumption. Keep the result ready to use.