The Classification Prompt Pattern

Classification is one of the most useful things AI does for business operations. Take something unstructured, assign it to a category, and route it accordingly. The classification prompt pattern ai technique makes this reliable and consistent at any scale.

If it can be categorized, this pattern handles it.

The Pattern Structure

Every classification prompt has three parts.

Part 1: Define the categories. Be specific. "Classify this support ticket as: billing, technical, feature-request, account-access, or other." Clear categories with no overlap produce better results.

Part 2: Provide the input. The text, data, or content to classify.

Part 3: Request the output format. "Return the category name only." Or "Return the category and a confidence score from 0 to 100." Or "Return the category and a one-sentence justification."

Example: Lead Classification

Classify this lead into one of the following categories:
- hot: Ready to buy within 30 days, has budget and authority
- warm: Interested but needs nurturing, 1-3 month timeline
- cold: Researching, no immediate intent
- unqualified: Does not match our target market

Lead information: [paste form submission]

Return: category, confidence (0-100), one-sentence reasoning

This runs on every incoming lead. The output determines the routing. Hot leads get immediate outreach. Warm leads enter a nurture sequence. Cold leads get educational content. Unqualified leads get a polite redirect.

Example: Support Ticket Classification

Classify this support ticket:
Categories: billing, technical-bug, feature-request, account-access, general-inquiry
Priority: urgent, normal, low

Ticket: [paste ticket text]

Return: category, priority, suggested-assignee (billing-team, engineering, product, support)

The ticket is classified, prioritized, and routed in seconds.

Making Classification Reliable

The biggest failure mode is ambiguous categories. If a ticket could reasonably be "billing" or "account-access," the model will inconsistently assign it. Fix this by defining clear boundaries.

"Billing: questions about charges, invoices, refunds, and payment methods. Account-access: questions about login, password reset, and permissions. If a ticket involves both a billing issue AND an access issue, classify as billing."

Rules for edge cases prevent inconsistency.

Scaling Classification

Classification works at any volume. One lead or one thousand leads, the pattern is the same. The cost per classification with a mini model is fractions of a cent. Processing thousands of items per day costs dollars, not hundreds.

This is the foundation of automated operations. Classify first, then route, then act. The classification pattern makes the rest possible.

Build These Systems

Ready to implement? These step-by-step tutorials show you exactly how:

• How to Build an AI Ticket Classification System - Classify and route support tickets to the right team automatically using AI.
• How to Build a Customer Issue Pattern Detector - Detect trending support issues before they become widespread problems.
• How to Build Few-Shot Prompts for Consistent Output - Use example-based prompting to get reliable, formatted AI responses every time.