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How to classify support tickets with LLM

Q: How to classify support tickets with LLM

Use a large language model like gpt-4o via the OpenAI Python SDK to classify support tickets by sending the ticket text as a prompt and instructing the model to output the category. This approach requires crafting a clear prompt and parsing the model's response for the classification label.

Quick answer

Use a large language model like gpt-4o via the OpenAI Python SDK to classify support tickets by sending the ticket text as a prompt and instructing the model to output the category. This approach requires crafting a clear prompt and parsing the model's response for the classification label.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0

Setup

Install the official openai Python SDK and set your API key as an environment variable for secure authentication.

bash

pip install openai>=1.0

output

Collecting openai
  Downloading openai-1.x.x-py3-none-any.whl
Installing collected packages: openai
Successfully installed openai-1.x.x

Step by step

This example shows how to classify a support ticket into categories like "Billing", "Technical", or "General" using gpt-4o. The prompt instructs the model to return only the category name.

python

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

support_ticket = "My internet connection is slow and keeps dropping intermittently."

prompt = f"Classify the following support ticket into one of these categories: Billing, Technical, General.\nTicket: {support_ticket}\nCategory:" 

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": prompt}]
)

category = response.choices[0].message.content.strip()
print(f"Support ticket category: {category}")

output

Support ticket category: Technical

Common variations

Use gpt-4o-mini for faster, cheaper classification with slightly less accuracy.
Implement async calls with asyncio and await for high-throughput classification.
Use streaming to process large batches interactively.

python

import os
import asyncio
from openai import OpenAI

async def classify_ticket_async(ticket_text: str) -> str:
    client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
    prompt = f"Classify the following support ticket into Billing, Technical, or General.\nTicket: {ticket_text}\nCategory:"
    response = await client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content.strip()

async def main():
    ticket = "I need help updating my payment method."
    category = await classify_ticket_async(ticket)
    print(f"Async classified category: {category}")

if __name__ == "__main__":
    asyncio.run(main())

output

Async classified category: Billing

Troubleshooting

If the model returns unexpected text, ensure your prompt clearly instructs it to output only the category name.
If you get authentication errors, verify your OPENAI_API_KEY environment variable is set correctly.
For rate limits, implement exponential backoff or use a smaller model like gpt-4o-mini.

✅

Key Takeaways

Use clear, concise prompts instructing the LLM to output only the classification label.
The OpenAI Python SDK with gpt-4o or gpt-4o-mini models is ideal for support ticket classification.
Async and streaming calls improve throughput for batch classification tasks.
Always secure your API key via environment variables to avoid leaks.
Prompt engineering is critical to get consistent, parseable classification results.

Verified 2026-04 · gpt-4o, gpt-4o-mini

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