Hey everyone, I'm mostly working on cloud stuff but my marketing team asked me to look into LangChain. They get hundreds of support tickets and blog pitches that need sorting into categories. Manual tagging is a huge time sink.
I'm trying to see if LangChain can automate this. I've only used it for simple QA with a PDF. For categorization, would you chain a prompt to an LLM like this?
```python
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
template = """Classify this text into one of these categories: [Feature Request, Bug Report, General Inquiry, Spam].
Text: {text}
Category:"""
prompt = PromptTemplate(template=template, input_variables=["text"])
```
Is this the right approach? Also, how do you handle when the text doesn't fit any category well? Would you use a separate chain to check confidence? Any examples or gotchas would be super helpful.
Your core approach is spot on. Starting with a straightforward prompt chain is exactly how I'd begin. However, moving from a demo script to a production workflow introduces a few wrinkles.
You asked about handling ambiguous texts. I'd suggest embedding a "confidence check" directly into your prompt template. Instead of a separate chain, I add instructions like: "If the text is unclear, output 'Uncertain: [Reason]'". This gives you a single, consistent output format and you can later filter those for manual review. I've found LLMs are surprisingly good at self-assessing when explicitly asked.
One gotcha with your categories: "Spam" can be tricky because it often requires external context (like sender reputation) that isn't in the text itself. You might get false positives. I'd recommend starting with just the three substantive categories and filtering spam with a simpler rule-based system first. Also, be mindful of token usage when processing hundreds of tickets; batching your calls is a must. Good luck
Totally agree about the confidence check trick. It saved my team last month when we tried classifying deployment logs for anomalies. The LLM would spit out "Uncertain: timezone formatting mismatch" and that alone gave us a clear queue for a human to spot a real config bug.
Your spam point is gold. I once built a classifier that flagged every mention of "free trial" as spam, including legit customer complaints about their free trial expiring. Chaos. Marketing was not happy. Rule-based first, fancy AI second.
It's always DNS.
That's a solid starting point. Your template will work, but I'd tighten it up for production. The bare "Category:" at the end can sometimes lead to the LLM adding commentary. I prefer a more structured output format.
Try forcing a single-token response by using something like this:
```python
template = """Classify the text into exactly one category: Feature Request, Bug Report, General Inquiry, Spam, or Uncertain.
Text: {text}
Output only the category name."""
```
It reduces variability. On the confidence question, I'd advise against a separate chain for efficiency and cost. Baking the "Uncertain" option into the main prompt, like you and user309 suggested, is the way to go. Just remember to log the raw text whenever that label is applied, because you'll need it for later review and potential model fine-tuning.
My main gotcha for you, coming from a secrets management angle, is that your API key for OpenAI (or whichever LLM you use) will be sitting in that script. Please don't hardcode it. Use environment variables or, better yet, pull it from a vault at runtime. It's easy to forget that when you're prototyping a chain, but it's critical for ops.
Encrypt all the things.