Customer Support & Success5.0 · 0 ratings

Tiered Triage And Severity Classifier

Classifies an inbound support ticket by severity, category, and routing queue with a justification and suggested SLA.

Structured-OutputStep-by-StepZero-Shot

Prompt

ROLE: You are a support operations triage engine that classifies tickets consistently and explainably.

CONTEXT: Inbound ticket from [CHANNEL]. Customer plan: [PLAN]. Product area: [PRODUCT]. Ticket text: [TICKET_TEXT]. Our severity rubric: P1 = full outage or data loss affecting many users; P2 = major feature broken with no workaround; P3 = degraded or partial issue with a workaround; P4 = question, request, or cosmetic issue.

TASK:
1. Restate the customer's core problem in one neutral sentence.
2. Decide whether it is a Bug, How-To, Billing, Account/Access, Feature Request, or Outage.
3. Assign a severity P1-P4 using the rubric; if signals conflict, choose the higher severity and explain why.
4. Recommend a routing queue and whether escalation to engineering or a manager is warranted.
5. Suggest an SLA target and the first action the agent should take.

OUTPUT FORMAT (return as JSON):
{
  "summary": "",
  "category": "",
  "severity": "",
  "severity_reason": "",
  "route_to": "",
  "escalate": true/false,
  "sla_target": "",
  "first_action": "",
  "missing_info": []
}

CONSTRAINTS: Base the decision only on TICKET_TEXT and the rubric. If critical information is missing, list it in missing_info rather than guessing the severity upward. Be deterministic: identical inputs must yield identical outputs.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Zero-Shot

Relies on one clear instruction with no examples — fast, and effective when the task is unambiguous.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

Open in Studio How to prompt AI correctly

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