Rapid Ticket Intake & Smart Classification (Email/Chat → Intent & Priority → Queue Assignment)
Classify incoming tickets by intent, urgency, product area, and risk so frontline teams can auto-route work and protect SLAs on day one.
Prompt Overview
Tips For You
- Set your intent taxonomy once, then train macros to map outputs to helpdesk queues. - Track disagreements between AI labels and human overrides to refine rules. - Add a regex pre-check for account or region markers to boost routing accuracy.
From Operations TeamNexusAi TechnologyProblem It Solves
Unstructured queues slow response and bury urgent items. This prompt produces consistent, auditable labels that drive routing and prioritization.
Intent taxonomy lock-in
Consistent labels that won’t drift across agents or shifts.
Priority with evidence
Clear quotes back every priority decision.
Risk flagging
Spot legal, security, outage, and churn signals instantly.
Queue recommendation
Maps each ticket to the right team with rationale.
AI Prompt Instructions
Act as: A senior support operations analyst configuring first-line AI triage for a multi-channel helpdesk.
Why this task matters: Accurate upfront classification reduces handle time, protects SLAs, and prevents high-risk issues from getting stuck in general queues.
Important boundaries:
- Never fabricate account data or promises. If missing info, flag as Missing Context.
- Respect policy and security; do not include secrets in outputs.
- Keep labels stable and machine-readable.
User inputs (paste below):
- Customer message (raw)
- Optional metadata: channel, customer tier, product, region, language
- Known SLAs by tier (if any)
Objectives:
1) Identify intent (billing, technical, account access, bug report, feature request, complaint, other).
2) Assign priority (P0 critical, P1 high, P2 normal, P3 low) with justification.
3) Detect risk flags (legal, security, outage, churn, VIP).
4) Suggest routing queue (Billing, Tech L1, Tech L2, CS, Trust & Safety, Escalations).
5) List missing context questions to unblock resolution.
Analysis workflow:
1) Parse message for explicit asks, symptoms, timeline, and sentiment.
2) Map to a single primary intent and up to two secondary intents.
3) Evaluate priority using SLA/tier, impact, and time-sensitivity.
4) Check for risk flags and explain if any apply.
5) Recommend target queue and rationale.
6) Draft 3 clarifying questions if information is insufficient.
Required output format (JSON):
{
"intent_primary": "",
"intent_secondary": [""],
"priority": "P0|P1|P2|P3",
"risk_flags": ["security|legal|outage|vip|churn|none"],
"route_queue": "",
"sla_note": "",
"evidence": ["short quotes from the message"],
"missing_context_questions": ["?", "?", "?"]
}
Quality controls:
- Support each label with at least one short quote.
- If evidence is weak, lower confidence and include a missing context question.
Verification checklist:
- Does priority match impact and tier?
- Are risk flags justified by quotes?
- Is the route_queue aligned with intent?
Final instruction: Produce only the JSON block.
Expected Outcome
{ "intent_primary": "billing", "intent_secondary": ["refund"], "priority": "P1", "risk_flags": ["churn"], "route_queue": "Billing", "sla_note": "Tier Gold: 4h first response", "evidence": ["charged twice", "cancel if not fixed"], "missing_context_questions": ["What invoice ID?", "Which charge date?", "Preferred refund method?"] }
Implementation Journey
Classify new tickets in ChatGPT
Open ChatGPT and paste 10–20 recent customer messages with any known SLA notes. Run the prompt to generate JSON labels for each ticket with intent, priority, risk flags, and route_queue.
10-15 minMap labels to queues
In ChatGPT, ask for a mapping table from route_queue to your helpdesk queues. Confirm the mapping and regenerate JSON with the correct queue names for your environment.
5-10 minDeploy routing rules
Use the final JSON as your spec to create routing rules in your helpdesk. Test with 20 fresh tickets and compare AI vs human routing to verify accuracy before enabling auto-assign.
20-30 min