Recommendation
Use Zapier to extract incoming support email text, call ChatGPT with a low-temperature JSON-output prompt that returns: short summary, priority (P1/P2/P3), required tags, suggested assignee/team, confidence score, and escalation flag. Map that JSON into Zendesk fields. Add fallbacks: keyword rules, auto-queue to human reviewers, and alerts when confidence is low.
Why this works
A structured (machine-readable) prompt + low temperature keeps outputs deterministic and easy to map into Zendesk. Zapier handles triggers and field mapping; ChatGPT handles natural-language understanding and triage.
Prompt structure (use in Zapier “Call OpenAI” step)
System message: You are a support triage assistant. Always return ONLY valid JSON matching the schema below. Be concise.
User message: "Email:
JSON schema:
{
"summary": "one-sentence summary (<=20 words)",
"priority": "P1|P2|P3",
"tags": ["string"],
"assignee_team": "string",
"confidence": number (0-1),
"escalate": true|false,
"rationale": "one-line reason"
}
Instructions: Prioritize safety/availability issues as P1. If user mentions outage, data loss, security breach → escalate:true and priority P1. Set confidence low (<0.6) if the email is ambiguous or truncated. Temperature: 0–0.2. Max tokens: limit to avoid cost (e.g., 200)."
Few-shot addition (optional): include 2 quick examples mapped to the schema to improve consistency.
Fallbacks and error handling
- If OpenAI call fails or output is invalid JSON: Tag as "triage_failed"; create Zendesk ticket with default priority P2 and route to the triage queue. Send a Slack/DM alert to on-duty triage.
- If confidence batch estimates of API spend. If you have many agents, route low-confidence tickets to a smaller specialist triage team. For starting point, use ChatGPT via Zapier and tune prompts for your ticket mix.
If you want, I can draft a concrete Zapier/OpenAI prompt with two examples tailored to your common ticket types (billing, outage, login issues).
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