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

Large Language Models (LLMs) have revolutionized classification tasks, especially where traditional machine learning struggles with complex business rules or limited training data. In this guide, you'll build a production-ready insurance support ticket classifier using Claude that achieves over 95% accuracy.

Why Use Claude for Classification?

Traditional ML classifiers require extensive labeled datasets and struggle with nuanced business logic. Claude excels here because:

• Handles complex rules: Understands subtle distinctions between categories (e.g., "billing inquiry" vs. "coverage explanation")
• Works with limited data: Performs well even with just 50-100 labeled examples per category
• Provides explanations: Returns natural language justifications for each classification
• Easily adaptable: Update categories or rules by modifying the prompt, not retraining models

Prerequisites

• Python 3.11+
• Anthropic API key (get one here)
• VoyageAI API key (optional - embeddings are pre-computed in the cookbook)
• Basic understanding of classification problems

Step 1: Setup and Data Preparation

First, install the required packages:

pip install anthropic voyageai pandas matplotlib scikit-learn numpy

Load your API keys and prepare your environment:

import os
import anthropic
Set your API keys
os.environ["ANTHROPIC_API_KEY"] = "your-api-key-here"
os.environ["VOYAGE_API_KEY"] = "your-voyage-api-key"  # Optional
client = anthropic.Anthropic()
MODEL_NAME = "claude-3-opus-20240229"  # Or claude-3-sonnet for speed

Understanding the Data

For this guide, we'll use synthetically generated insurance support tickets across 10 categories:

• Billing Inquiries - Questions about invoices, charges, premiums
• Policy Administration - Policy changes, cancellations, renewals
• Claims Assistance - Claims process, documentation, status
• Coverage Explanations - What's covered, limits, exclusions
• Account Management - Login issues, profile updates
• Agent/Representative - Finding agents, contacting reps
• Complaints/Escalations - Dissatisfaction, formal complaints
• Policy Recommendations - New coverage suggestions
• Fraud and Compliance - Suspicious activity, regulatory questions
• General Inquiries - Miscellaneous questions

Step 2: Baseline Classification with Zero-Shot Prompting

Let's start with a simple zero-shot approach to establish a baseline:

def classify_ticket_zeroshot(ticket_text, categories):
    prompt = f"""You are an insurance support ticket classifier. 
Classify the following ticket into exactly one of these categories:
Categories:
{categories}
Ticket: {ticket_text}
Respond with only the category name."""
    
    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 works for obvious cases but struggles with nuanced distinctions.

Step 3: Improving Accuracy with Few-Shot Examples

Adding examples dramatically improves performance. Here's how to structure your prompt:

def classify_ticket_fewshot(ticket_text, categories, examples):
    example_text = ""
    for ex in examples:
        example_text += f"Ticket: {ex['text']}\nCategory: {ex['category']}\n\n"
    
    prompt = f"""You are an insurance support ticket classifier. 
Classify the following ticket into exactly one category.
Categories:
{categories}
Here are some examples:
{example_text}
Ticket to classify: {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%. The key is selecting diverse, high-quality examples.

Step 4: Implementing Retrieval-Augmented Generation (RAG)

For maximum accuracy, 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
vo = voyageai.Client(api_key=os.environ["VOYAGE_API_KEY"])
def get_embedding(text):
    result = vo.embed([text], model="voyage-2")
    return result.embeddings[0]
Pre-compute embeddings for your training data
training_embeddings = [get_embedding(ex["text"]) for ex in training_data]
def find_similar_examples(query, training_data, training_embeddings, k=3):
    query_emb = get_embedding(query)
    similarities = cosine_similarity([query_emb], 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, categories, training_data, training_embeddings):
    # Retrieve most similar examples
    similar_examples = find_similar_examples(
        ticket_text, training_data, training_embeddings, k=3
    )
    
    # Build prompt with retrieved examples
    example_text = ""
    for ex in similar_examples:
        example_text += f"Ticket: {ex['text']}\nCategory: {ex['category']}\n\n"
    
    prompt = f"""You are an insurance support ticket classifier.
Classify the following ticket into exactly one category.
Categories:
{categories}
Here are the most relevant examples:
{example_text}
Ticket to classify: {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: ~90-92%. RAG ensures you always show the most relevant examples.

Step 5: Adding Chain-of-Thought Reasoning

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

def classify_ticket_cot(ticket_text, categories, training_data, training_embeddings):
    similar_examples = find_similar_examples(
        ticket_text, training_data, training_embeddings, k=3
    )
    
    example_text = ""
    for ex in similar_examples:
        example_text += f"Ticket: {ex['text']}\nCategory: {ex['category']}\n\n"
    
    prompt = f"""You are an insurance support ticket classifier.
Classify the following ticket into exactly one category.
Categories:
{categories}
Relevant examples:
{example_text}
Ticket to classify: {ticket_text}
First, think step-by-step about which category fits best. 
Consider: What is the customer's main request? What keywords match? 
Then provide your final answer in this format:
Reasoning: [your step-by-step reasoning]
Category: [exact category name]"""
    
    response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=200,
        messages=[{"role": "user", "content": prompt}]
    )
    
    # Parse the response
    full_response = response.content[0].text.strip()
    category = full_response.split("Category:")[-1].strip()
    return category

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

Step 6: Testing and Evaluation

Here's how to evaluate your classifier:

from sklearn.metrics import accuracy_score, classification_report
def evaluate_classifier(test_data, classifier_fn, categories, training_data, training_embeddings):
    predictions = []
    true_labels = []
    
    for ticket in test_data:
        pred = classifier_fn(
            ticket["text"], 
            categories, 
            training_data, 
            training_embeddings
        )
        predictions.append(pred)
        true_labels.append(ticket["category"])
    
    accuracy = accuracy_score(true_labels, predictions)
    print(f"Accuracy: {accuracy:.2%}")
    print("\nClassification Report:")
    print(classification_report(true_labels, predictions))
    
    return accuracy

Production Considerations

When deploying this system:

• Cache embeddings: Store pre-computed embeddings in a vector database (Pinecone, Weaviate, etc.)
• Batch processing: Use Claude's batch API for high-volume classification
• Confidence thresholds: Set a minimum confidence score; flag low-confidence tickets for human review
• Feedback loop: Log misclassifications to continuously improve your example set
• Cost optimization: Use Claude 3 Haiku for simple tickets, Sonnet for complex ones

Key Takeaways

• Start simple, iterate fast: Begin with zero-shot prompting (70% accuracy), then add few-shot examples (80%), RAG (90%), and chain-of-thought (95%+) as needed
• RAG is your secret weapon: Dynamically retrieving the most relevant examples for each query dramatically improves accuracy without manual prompt engineering
• Chain-of-thought reasoning adds 5-10% accuracy: Having Claude explain its reasoning before giving the final answer catches edge cases and ambiguous tickets
• Explainability is built-in: Unlike traditional ML classifiers, Claude provides natural language justifications for every classification, making it audit-ready
• Adaptable to any domain: This pattern works for any classification task - customer support, content moderation, document routing, and more