> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getsnippets.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Classification

Text classification is the process of assigning predefined categories or labels to text. It's a foundational task in natural language processing and one that LLMs can perform with remarkable accuracy. You can use classification for a huge range of applications, including sentiment analysis of customer feedback, topic categorization of news articles, spam detection in emails, and intent recognition in user queries.

### The Best Tool for the Job: Few-Shot Prompting

While you can sometimes get away with a zero-shot prompt for very simple classification tasks (e.g., "Is this review positive or negative?"), the most robust and reliable method is **few-shot prompting**.

As we covered in the Core Principles section, few-shot prompting allows you to "teach" the model the exact classification system you want it to use. This is critical because classification is often subjective and context-dependent. By providing clear examples, you remove ambiguity and ensure the model's output aligns with your specific needs.

### From Simple to Complex Classification: A Case Study

Let's look at how we can use few-shot prompting to build a sophisticated classifier for customer support tickets.

**Goal:** We want to classify incoming support tickets into three categories: `Technical Issue`, `Billing Inquiry`, and `General Question`.

**A Good Few-Shot Prompt:**

```
Please classify the following customer support tickets into one of three categories: Technical Issue, Billing Inquiry, or General Question.

Ticket: "Hi, I can't seem to log in to my account. I've reset my password but it's still not working."
Category: Technical Issue

Ticket: "Hello, I was wondering if you offer any discounts for non-profit organizations?"
Category: General Question

Ticket: "I think I was overcharged on my last invoice. Can you please check?"
Category: Billing Inquiry

Ticket: "My dashboard is showing an error message and I can't access my reports."
Category:
```

*This prompt is effective because it provides one clear example for each category. The model will see the pattern and correctly classify the final ticket as `Technical Issue`.*

**An Advanced Prompt with Edge Case Handling:**
Sometimes, a ticket might fit into more than one category. We can teach the model how to handle this.

```
Please classify the following customer support tickets. You can assign one or more of the following categories: Technical Issue, Billing Inquiry, General Question. Format the output as a JSON array.

Ticket: "Hi, I can't seem to log in to my account. I've reset my password but it's still not working."
Category: ["Technical Issue"]

Ticket: "I think I was overcharged on my last invoice. Can you also tell me what your business hours are?"
Category: ["Billing Inquiry", "General Question"]

Ticket: "My dashboard is showing an error message and I can't access my reports. This is preventing me from upgrading my account, which I'd like to do today."
Category:
```

*This is a much more sophisticated prompt. We've instructed the model to handle multiple categories and to output the result in a machine-readable JSON format. We also provided a tricky example that combines a billing and a general question. The model will now correctly classify the final ticket as `["Technical Issue", "Billing Inquiry"]`.*

### A Toolkit of Classification Techniques

* **Chain of Thought Classification:** For very complex classification tasks, you can ask the model to "think step by step."
  * `For the following user comment, first identify the main topic of the comment, then decide if the sentiment is positive, negative, or neutral. Finally, assign one of the following categories...`
* **Fine-Grained Classification:** Don't be afraid to use a large number of categories. LLMs can handle dozens or even hundreds of categories if you provide clear examples.
* **Confidence Scoring:** For more advanced use cases, you can ask the model to provide a confidence score for its classification.
  * `Classify the following text and provide a confidence score (from 0.0 to 1.0) for your answer.`

By leveraging few-shot prompting and these advanced techniques, you can build powerful and nuanced text classifiers for almost any application.