Building a High-Accuracy Insurance Ticket Classifier with Claude: From 70% to 95%+ Accuracy

Classification is one of the most practical and impactful applications of Large Language Models (LLMs) in enterprise settings. While traditional machine learning models struggle with complex business rules, limited training data, and the need for explainable results, Claude excels in all these areas.

In this guide, you'll build a production-ready classification system that categorizes insurance support tickets into 10 distinct categories. You'll learn how to progressively improve accuracy from a baseline of ~70% to over 95% by combining three powerful techniques: prompt engineering, retrieval-augmented generation (RAG), and chain-of-thought reasoning.

Prerequisites

Before diving in, make sure you have:

• Python 3.11+ installed
• An Anthropic API key (required)
• A VoyageAI API key (optional—embeddings can be pre-computed)
• Basic familiarity with classification problems
• Understanding of Python and API usage

Why Use Claude for Classification?

Traditional machine learning approaches to classification face three major challenges:

• Complex business rules: Insurance policies have nuanced conditions that are hard to encode in feature vectors
• Limited training data: Many real-world scenarios don't have thousands of labeled examples
• Lack of explainability: Black-box models can't justify why a ticket was classified a certain way

Claude addresses all three. It can understand natural language descriptions of business rules, perform well with few-shot examples, and provide clear reasoning for every classification decision.

Setting Up Your Environment

First, install the required packages:

pip install anthropic voyageai pandas matplotlib scikit-learn numpy

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

import os
from anthropic import Anthropic
Load API keys from environment variables
anthropic_api_key = os.environ.get("ANTHROPIC_API_KEY")
client = Anthropic(api_key=anthropic_api_key)
Set your model
MODEL_NAME = "claude-3-opus-20240229"  # Or claude-3-sonnet for faster/cheaper

Step 1: Define Your Classification Problem

For this guide, we'll use a synthetic dataset of insurance support tickets with 10 categories. Here are the category definitions:

Step 2: Baseline Classification with Zero-Shot Prompting

Let's start with a simple zero-shot approach. This establishes our baseline accuracy:

def classify_ticket_zero_shot(ticket_text: str) -> str:
    prompt = f"""You are an insurance support ticket classifier. 
Classify the following ticket into exactly one of these categories:
Billing Inquiries
Policy Administration
Claims Assistance
Coverage Explanations
Account Management
Agent Assistance
Technical Support
Fraud Concerns
Complaints and Feedback
General Inquiries

Respond with ONLY the category name, nothing else.
Ticket: {ticket_text}"""
    
    response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=50,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.content[0].text.strip()

Expected accuracy: ~70-75%. This is decent but not production-ready.

Step 3: Improve with Few-Shot Prompting

Adding a few carefully chosen examples dramatically improves accuracy:

def classify_ticket_few_shot(ticket_text: str, examples: list) -> str:
    # Build examples into the prompt
    example_text = ""
    for i, ex in enumerate(examples[:5]):  # Use 5 examples
        example_text += f"Example {i+1}:\nTicket: {ex['text']}\nCategory: {ex['category']}\n\n"
    
    prompt = f"""You are an insurance support ticket classifier. 
Here are examples of correctly classified tickets:
{example_text}
Now classify this ticket:
Ticket: {ticket_text}
Category:"""
    
    response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=50,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.content[0].text.strip()

Expected accuracy: ~80-85%. Better, but we can go higher.

Step 4: Implement Retrieval-Augmented Generation (RAG)

This is where things get powerful. Instead of static examples, we dynamically retrieve the most relevant examples for each ticket using vector embeddings:

import voyageai
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
Initialize VoyageAI client
vo = voyageai.Client(api_key=os.environ.get("VOYAGE_API_KEY"))
Create embeddings for your training data
def embed_texts(texts: list) -> np.ndarray:
    result = vo.embed(texts, model="voyage-2")
    return np.array(result.embeddings)
Store training embeddings
training_texts = [ex["text"] for ex in training_data]
training_embeddings = embed_texts(training_texts)
def find_similar_examples(query: str, k: int = 3) -> list:
    query_embedding = embed_texts([query])
    similarities = cosine_similarity(query_embedding, training_embeddings)[0]
    top_indices = np.argsort(similarities)[-k:][::-1]
    return [training_data[i] for i in top_indices]
def classify_ticket_rag(ticket_text: str) -> str:
    # Retrieve most similar examples
    similar_examples = find_similar_examples(ticket_text, k=3)
    
    # Build prompt with retrieved examples
    example_text = ""
    for i, ex in enumerate(similar_examples):
        example_text += f"Example {i+1}:\nTicket: {ex['text']}\nCategory: {ex['category']}\n\n"
    
    prompt = f"""You are an insurance support ticket classifier. 
Here are the most relevant examples for this ticket:
{example_text}
Classify this ticket:
Ticket: {ticket_text}
Category:"""
    
    response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=50,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.content[0].text.strip()

Expected accuracy: ~90-93%. The dynamic retrieval ensures Claude always has the most relevant context.

Step 5: Add Chain-of-Thought Reasoning

For the final accuracy boost, ask Claude to reason step-by-step before giving the answer:

def classify_ticket_rag_cot(ticket_text: str) -> dict:
    similar_examples = find_similar_examples(ticket_text, k=3)
    
    example_text = ""
    for i, ex in enumerate(similar_examples):
        example_text += f"Example {i+1}:\nTicket: {ex['text']}\nCategory: {ex['category']}\n\n"
    
    prompt = f"""You are an insurance support ticket classifier. 
Here are the most relevant examples:
{example_text}
Classify this ticket. First, think step-by-step about why it fits a particular category, then provide your final answer.
Ticket: {ticket_text}
Reasoning:"""
    
    response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=200,
        messages=[{"role": "user", "content": prompt}]
    )
    
    full_response = response.content[0].text.strip()
    
    # Parse reasoning and final answer
    # (In practice, you'd use structured output or parsing logic)
    return {
        "full_response": full_response,
        "category": extract_category(full_response)  # Custom parsing function
    }

Expected accuracy: 95%+. The chain-of-thought reasoning helps Claude handle edge cases and ambiguous tickets.

Evaluating Your Classifier

Here's how to systematically evaluate performance:

from sklearn.metrics import accuracy_score, classification_report
def evaluate_classifier(classify_fn, test_data: list) -> dict:
    predictions = []
    actuals = []
    
    for item in test_data:
        pred = classify_fn(item["text"])
        predictions.append(pred)
        actuals.append(item["category"])
    
    accuracy = accuracy_score(actuals, predictions)
    report = classification_report(actuals, predictions)
    
    return {
        "accuracy": accuracy,
        "report": report
    }
Run evaluation
results = evaluate_classifier(classify_ticket_rag_cot, test_data)
print(f"Accuracy: {results['accuracy']:.2%}")
print(results['report'])

Performance Comparison

Production Considerations

When deploying this system, keep these best practices in mind:

• Cache embeddings: Pre-compute and store embeddings for your training data to reduce latency
• Use structured output: With Claude's JSON mode or tool use, enforce a structured response format
• Monitor confidence: Track cases where Claude is uncertain and route them for human review
• Handle edge cases: Add a "Needs Review" category for tickets that don't clearly fit any category
• Iterate on examples: Regularly update your training data with misclassified tickets

Key Takeaways

• Start simple, then layer complexity: Begin with zero-shot prompting, then add few-shot examples, RAG, and chain-of-thought reasoning progressively. Each layer adds meaningful accuracy improvements.
• RAG dramatically improves accuracy: Dynamic retrieval of relevant examples outperforms static few-shot prompting by 10-15 percentage points, especially with larger training datasets.
• Chain-of-thought reasoning adds the final polish: Asking Claude to reason step-by-step before classifying helps handle edge cases and ambiguous tickets, pushing accuracy above 95%.
• Explainability is built-in: Unlike traditional ML classifiers, Claude can explain why it made each classification, which is critical for regulated industries like insurance.
• Production readiness requires more than accuracy: Consider latency, caching, structured output, and human-in-the-loop review for real-world deployment.