Resource
Text Classification Prompt — the Anatomy
The blocks a reliable classification prompt needs: defined labels, classification rules, edge-case rules, an ambiguity policy, and a confidence contract.
Classify Support Tickets with AI
Billing, Technical, Account, How-To, Feature Request — ticket triage with definitions that decide the borderline cases for the model.
Overview
Ticket triage fails at the borders: is a "payment page crashes" ticket Billing or Technical? Without definitions, the model decides differently every run. This setup classifies tickets into five routing categories whose one-sentence definitions draw the borders deliberately — How-To Question is defined as "nothing is broken" precisely to keep it apart from Technical — and reports a high/medium/low confidence with each label, so your queue can auto-route the confident calls and surface the uncertain ones to a human.
How to use this resource
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Generate and pipe tickets through
One ticket per call; the prompt reads the ticket text from the input below it.
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Check the border definitions
"Nothing is broken" in How-To Question and "product defects" in Technical are doing the triage — adjust them to where YOUR borders run.
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Route by confidence
high → straight to the queue; medium/low → human review. The confidence contract exists for exactly this split.
Why This Works
- Definitions written to exclude each other settle the borderline tickets
- Single-label edge rules force a primary-purpose decision instead of a coin flip
- Self-reported confidence makes partial automation safe — automate the easy 80%
Best for
- Support teams triaging by hand today
- Queues where Billing-vs-Technical borderline tickets cause bouncing
- Helpdesk automations with a classification step
Not for
- Pulling ticket fields (product, severity, summary) — that's the Extraction Prompt Generator
- Writing the reply — triage decides where, not what to answer
Use cases
- Routing inbound tickets to billing, technical, and account queues
- Keeping triage consistent across shifts, agents, and model runs
- Auto-routing high-confidence labels and reviewing low-confidence ones
FAQ
What does the support ticket classification prompt return for each ticket?
For each ticket it returns one label and a confidence, as a JSON object like {"label": "Billing", "confidence": "high"}. It picks from five routing categories — Billing, Technical, Account, How-To Question, Feature Request — by matching the ticket against each category's one-sentence definition, not the label name, and reads the whole ticket first because the deciding signal often comes late.
How does it classify a ticket that fits two categories, like "the payment page crashes"?
It picks the label matching the ticket's primary purpose — what the writer mainly wants — so "the payment page crashes" lands in Technical (a defect) rather than Billing, even though it mentions payment. When two definitions fit equally, it prefers the more specific one. The borders are set by the definitions themselves: How-To Question is defined as "nothing is broken" to keep it clear of Technical.
Can I change the categories to match my own support queues?
Yes — the five labels and their one-sentence definitions are the routing logic, so rewrite them to match how your own queues split work. The definitions do the triage, not the label names, so tighten each until the borderline tickets fall the way your team routes them. Regenerate the prompt in the Data Classification tool with your own labels and rules.
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The JSON won't parse and you can't see why. Deterministic cause-sniffing — trailing commas, single quotes, unclosed brackets — and the repair prompt that fixes it.
Related tools
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Data Classification Prompt
Build classification prompts that assign labels from a closed set — with label definitions and edge-case rules.
Projects that use this resource
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Build an AI Support Agent with AI
The full path to a support agent you can put in front of customers — write its instructions, ground it in your docs, route and handle tickets, then evaluate and cost-control it before it goes live.
Project
Build a Customer Support System with AI
The full path to a support operation, not just a bot — stand up the knowledge base, route the tickets, add the AI agent, integrate your stack, close the feedback loop, evaluate, and deploy.
Workflows that use this resource
Workflow
AI Customer Support Workflow
Run inbound support the same way every time — triage and route the ticket, pull the details that matter, draft a reply in a consistent voice, and log the resolution for the record.
Guides for this resource
Guide
Classify and Tag Data With AI Consistently
AI tags one batch pricing, billing, bug — then tags the same items cost, plan confusion, error next week, so every count measures a moving target. Here's how to classify and tag data consistently: fix a taxonomy, define every label, and hold the rules steady across runs.