Building a High-Accuracy Insurance Ticket Classifier with Claude

Classification is one of the most common and valuable tasks in business automation. Whether you're routing support tickets, categorizing customer feedback, or flagging compliance issues, getting the classification right is critical. Traditional machine learning approaches often struggle with complex business rules, limited training data, and the need for explainable results.

Large Language Models (LLMs) like Claude have changed the game. They can handle nuanced business logic, work with minimal examples, and provide natural language explanations for their decisions. In this guide, you'll build a production-ready insurance support ticket classifier that achieves 95%+ accuracy by combining three powerful techniques: prompt engineering, retrieval-augmented generation (RAG), and chain-of-thought reasoning.

What You'll Learn

By the end of this guide, you'll know how to:

• Design a classification system using Claude's API
• Use RAG to boost accuracy with limited training data
• Implement chain-of-thought reasoning for explainable results
• Evaluate and iteratively improve your classifier's performance

Prerequisites

• Python 3.11+ with basic familiarity
• An Anthropic API key (get one here)
• A VoyageAI API key (optional—embeddings are pre-computed)
• Basic understanding of classification problems

Step 1: Setup and Installation

First, install the required packages:

pip install anthropic voyageai pandas matplotlib scikit-learn numpy

Next, load your API keys and set up the Claude client:

import os
from anthropic import Anthropic
Load API keys from environment variables
ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY")
VOYAGE_API_KEY = os.environ.get("VOYAGE_API_KEY")
Initialize the Claude client
client = Anthropic(api_key=ANTHROPIC_API_KEY)
MODEL_NAME = "claude-3-opus-20240229"

Step 2: Problem Definition

We'll build a classifier for insurance support tickets. The dataset—synthetically generated by Claude 3 Opus—contains 10 categories:

• Billing Inquiries – Questions about invoices, charges, fees, and premiums
• Policy Administration – Requests for policy changes, updates, or cancellations
• Claims Assistance – Questions about the claims process and filing procedures
• Coverage Explanations – Questions about what is covered under specific policy types
• Account Management – Requests for account updates, password resets, or login issues
• Document Requests – Requests for policy documents, certificates, or ID cards
• Complaints – Customer complaints about service, delays, or disputes
• Fraud Reporting – Reports of suspected fraudulent activity
• Agent Assistance – Requests for agent contact or escalation
• General Inquiries – Miscellaneous questions not covered above

Step 3: Data Preparation

Prepare your training and test datasets. The training data will be used to build the classifier, while the test data evaluates its performance.

import pandas as pd
from sklearn.model_selection import train_test_split
Load your dataset (example structure)
df = pd.read_csv("insurance_tickets.csv")
X_train, X_test, y_train, y_test = train_test_split(
    df["ticket_text"], 
    df["category"], 
    test_size=0.2, 
    random_state=42
)

Step 4: Prompt Engineering

The key to high accuracy is a well-structured prompt. Here's the template we'll use:

def build_classification_prompt(ticket_text, examples, categories):
    """
    Build a prompt for Claude with examples and chain-of-thought reasoning.
    """
    prompt = f"""You are an expert insurance support ticket classifier. Your task is to categorize the following support ticket into one of these categories:
{categories}
Here are some examples to guide your classification:
{examples}
Now, classify this ticket:
Ticket: {ticket_text}
First, think step-by-step about the key elements in this ticket. Then, provide your final classification in this format:
Reasoning: [Your step-by-step reasoning]
Category: [Category name]
"""
    return prompt

Step 5: Implementing Retrieval-Augmented Generation (RAG)

RAG dramatically improves accuracy by retrieving the most similar examples from your training data and including them in the prompt. This is especially powerful when you have limited training data.

Create Embeddings

import voyageai
vo = voyageai.Client(api_key=VOYAGE_API_KEY)
Generate embeddings for training data
train_embeddings = vo.embed(
    X_train.tolist(), 
    model="voyage-2", 
    input_type="document"
).embeddings

Build a Retrieval Function

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
def retrieve_similar_examples(query, k=3):
    """
    Retrieve the k most similar training examples for a given query.
    """
    # Embed the query
    query_embedding = vo.embed(
        [query], 
        model="voyage-2", 
        input_type="query"
    ).embeddings[0]
    
    # Calculate cosine similarity
    similarities = cosine_similarity([query_embedding], train_embeddings)[0]
    
    # Get top k indices
    top_indices = np.argsort(similarities)[-k:][::-1]
    
    # Return the examples
    examples = []
    for idx in top_indices:
        examples.append({
            "text": X_train.iloc[idx],
            "category": y_train.iloc[idx],
            "similarity": similarities[idx]
        })
    return examples

Step 6: The Classification Function

Now combine everything into a single classification function:

def classify_ticket(ticket_text):
    """
    Classify an insurance support ticket using Claude with RAG.
    """
    # Retrieve similar examples
    similar_examples = retrieve_similar_examples(ticket_text, k=3)
    
    # Format examples for the prompt
    examples_text = ""
    for i, ex in enumerate(similar_examples, 1):
        examples_text += f"Example {i}:\nTicket: {ex['text']}\nCategory: {ex['category']}\n\n"
    
    # Build the prompt
    prompt = build_classification_prompt(
        ticket_text, 
        examples_text, 
        get_category_definitions()
    )
    
    # Call Claude
    response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=300,
        messages=[{"role": "user", "content": prompt}]
    )
    
    # Parse the response
    result = response.content[0].text
    return result

Step 7: Testing and Evaluation

Run your classifier against the test set and measure accuracy:

def evaluate_classifier(test_texts, test_labels):
    """
    Evaluate the classifier on test data.
    """
    correct = 0
    total = len(test_texts)
    
    for i, (text, true_label) in enumerate(zip(test_texts, test_labels)):
        result = classify_ticket(text)
        predicted_label = extract_category(result)
        
        if predicted_label == true_label:
            correct += 1
        
        if (i + 1) % 10 == 0:
            print(f"Processed {i+1}/{total} tickets...")
    
    accuracy = correct / total
    print(f"\nFinal Accuracy: {accuracy:.2%}")
    return accuracy
Run evaluation
accuracy = evaluate_classifier(X_test, y_test)

Step 8: Iterative Improvement

If your accuracy isn't where you want it, try these techniques:

• Increase the number of retrieved examples – Try k=5 or k=10
• Refine your category definitions – Make them more specific and include edge cases
• Add chain-of-thought instructions – Force Claude to reason step-by-step before outputting the category
• Fine-tune the prompt template – Experiment with different phrasing and formatting
• Use a more powerful model – Switch from Claude 3 Haiku to Claude 3 Opus for complex cases

Real-World Results

In testing, this approach consistently achieves:

• 70-80% accuracy with prompt engineering alone
• 85-90% accuracy with prompt engineering + RAG
• 95%+ accuracy with prompt engineering + RAG + chain-of-thought reasoning

Key Takeaways

• LLMs excel at complex classification – Claude handles nuanced business rules and limited training data better than traditional ML approaches
• RAG dramatically improves accuracy – Retrieving similar examples from your training data and including them in the prompt can boost accuracy by 15-20%
• Chain-of-thought reasoning adds explainability – Having Claude reason step-by-step before outputting a category not only improves accuracy but also makes the system auditable
• Iterative refinement is essential – Start simple, measure performance, and systematically improve your prompts, retrieval strategy, and model choice
• This pattern is reusable – The same architecture works for any classification problem: customer support routing, content moderation, document classification, and more