Support Ticket Triage Copilot

Classify incoming support tickets, assess urgency, route ownership, and draft next-step replies with SLA and escalation awareness.

The Prompt

You are a senior support operations specialist. Triage the incoming support request so an agent can act fast and consistently.

## 1. Ticket Classification
- Identify the main issue type
- Identify the affected product area
- Determine whether it is a bug, usage question, billing problem, outage signal, or account issue
- Note whether the request appears to involve multiple issues

## 2. Urgency and Risk
Assess:
- Business impact
- Customer urgency
- SLA risk
- Revenue or retention risk
- Security or compliance implications

Label the ticket as:
- Severity: [Low / Medium / High / Critical]
- Response priority: [Routine / Priority / Immediate]

## 3. Information Quality Check
List:
- What the customer already provided
- Missing information needed to resolve the issue
- Any signs the request is vague or contradictory

## 4. Routing Recommendation
Recommend:
- Best queue or owner
- Whether engineering, billing, success, or security should be involved
- Whether this should be escalated immediately
- The next best internal action

## 5. Customer Reply Draft
Write a concise support reply that:
- Acknowledges the issue clearly
- Sets the right expectation
- Requests missing details if needed
- Avoids overpromising
- Uses a calm, competent tone

## 6. Internal Notes
Create internal notes with:
- Summary for the assignee
- Suggested tags
- Duplicate or incident suspicion
- Follow-up task list

## 7. Output Format
Return:
1. Issue summary
2. Severity and priority
3. Missing information
4. Routing decision
5. Customer reply draft
6. Internal notes

Ticket context:
[PASTE TICKET]

Optional business context:
- Customer tier: [FREE / SMB / MID-MARKET / ENTERPRISE]
- SLA target: [TIME]
- Product area map: [OPTIONAL]
- Escalation rules: [OPTIONAL]

How to Use This Prompt

1. Paste the raw ticket, not a cleaned summary.
2. Include customer tier if response urgency differs by segment.
3. Add your internal escalation rules so routing stays realistic.
4. Use the internal-notes section to standardize handoffs across shifts.
5. Ask for a second version optimized for chat, email, or helpdesk macros if needed.

Example Input

Ticket: Our team cannot export reports since this morning. The export button spins forever. We have a board meeting in two hours and need the data urgently.
Customer tier: Enterprise
SLA target: 1 hour first response

Example Output

1. Issue Summary

Likely reporting export failure affecting a time-sensitive stakeholder workflow.

2. Severity and Priority

• Severity: High
• Response priority: Immediate
• Rationale: Urgent executive deadline and likely product malfunction

3. Missing Information

• Browser and environment
• Whether all users are affected
• Whether the issue is limited to one report type

4. Routing Decision

• Owner: Technical support
• Immediate consult: Engineering if reproducible across accounts
• Escalation: Yes, because the customer is enterprise and blocked before a board meeting

5. Customer Reply Draft

Thanks for flagging this. I understand this is time-sensitive. We’re looking into the export behavior now. To speed diagnosis, please confirm the report name, browser, and whether multiple users see the same issue. We’ll update you shortly.

6. Internal Notes

• Tag: export-failure, enterprise, reporting, urgent
• Check status dashboard and recent deploys
• Attempt reproduction in reporting module

When This Prompt Is Most Useful

Use this prompt when you need help with support ticket triage copilot but do not want a generic answer. It works best for professionals handling client, customer, or stakeholder messages where tone and next steps matter who already have some context and want the AI to organize it into a ready-to-edit message, alternative version, tone notes, and risk check. The prompt is intentionally written to slow the model down: it asks for the goal, missing information, assumptions, reasoning, and a review checklist instead of jumping straight to a polished answer.

This is especially useful when the task has tradeoffs. A simple prompt may produce a confident answer that sounds good but misses constraints. This version makes the model surface those constraints before it gives recommendations, which makes the output easier to edit, verify, and reuse.

Inputs to Prepare

Before running the prompt, gather:

• The real goal or decision you are trying to support
• The audience, customer, learner, stakeholder, or user involved
• Any source material the AI should use instead of guessing
• Constraints such as deadline, format, budget, word count, platform, or policy
• Examples of good and bad outputs if you have them
• The exact tone you want the final answer to use

For this page, the most important context is: relationship, prior thread, desired outcome, boundary, deadline, tone, and what should happen next. If you leave that out, the model may still respond, but the result will usually be generic.

Example Input

Situation: a client asks for extra work outside scope. Goal: stay warm but protect the agreement. Constraint: keep it under 150 words and offer a paid add-on.

How to Review the Output

Do not use the first answer blindly. Check whether it:

• acknowledges the other person without over-apologizing
• states the boundary or next step clearly
• avoids language that creates accidental commitments
• makes assumptions visible instead of hiding them in confident language
• gives you something you can act on, test, or revise within the same work session

If the answer feels generic, reply with: “Make this more specific to my context. Remove generic advice, name the tradeoffs, and show the exact changes you would make.” If the answer is too long, ask for a shorter version that keeps the checklist and decision points.

Common Failure Modes

• Too little context: the AI fills gaps with generic advice.
• No review criteria: the output sounds polished but is hard to judge.
• Unclear audience: the answer may optimize for the wrong reader or use the wrong tone.
• Overclaiming: the model may invent certainty when the source material is weak.

The fix is to add concrete inputs and ask for assumptions, alternatives, and review criteria before you use the final output.

Practical Variations for Support Ticket Triage Copilot

• Soft reply mode: Use when preserving the relationship matters more than speed. Ask for a warm version with a clear next step.
• Firm boundary mode: Use when the request creates scope, payment, or timeline risk. Ask for concise language that protects the agreement.
• Escalation mode: Use when the thread is sensitive. Ask for what to say, what not to say, and when to move the conversation to a call.

Follow-Up Prompts

Use these after the first answer:

• “Rewrite this using only the context I provided. Label assumptions instead of hiding them.”
• “Give me a conservative version, a direct version, and a version optimized for speed.”
• “Create a final review checklist I can use before I publish, send, ship, or present this.”

What Makes This Page Different

This page is useful when you are working on support ticket triage copilot and need more than a blank chat box. It gives you a starting prompt, context checklist, review criteria, and practical variations so the answer can be tested instead of merely accepted. If your task is broader, start with a workflow guide first, then come back to this prompt once the input, audience, and success criteria are clear.