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

Classification is one of the most practical applications of Large Language Models (LLMs) in enterprise settings. Traditional machine learning approaches often 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-grade classification system that categorizes insurance support tickets into 10 distinct categories. You'll learn how to progressively improve classification 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, ensure you have:

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

Setup and Installation

First, install the required packages:

pip install anthropic voyageai pandas matplotlib scikit-learn numpy

Next, load your API keys and configure 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 cost efficiency

Problem Definition: Insurance Support Ticket Classifier

Insurance companies receive thousands of support tickets daily. Manually categorizing these tickets is slow, expensive, and error-prone. Our goal is to build an automated classifier that can handle:

• Complex business rules (e.g., a billing question about a claim-related charge)
• Limited training data (we'll work with just 100 labeled examples)
• Explainable results (Claude can explain why it chose a category)

The 10 Categories

Here are the categories we'll classify tickets into:

Step 1: Baseline Classification with Zero-Shot Prompting

Let's start with a simple zero-shot approach. We'll ask Claude to classify a ticket without any examples.

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 Support
Underwriting
Fraud & Compliance
Product Information
General Inquiries

Respond with ONLY the category name.
Ticket: {ticket_text}"""
    
    response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=50,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.content[0].text.strip()

Result: This approach typically achieves ~70% accuracy. It works for obvious cases but struggles with ambiguous tickets that span multiple categories.

Step 2: Improving Accuracy with Few-Shot Prompting

Adding a few carefully selected examples dramatically improves performance. Here's how to structure your few-shot prompt:

def classify_ticket_few_shot(ticket_text: str, examples: list) -> str:
    # Build examples string
    examples_text = ""
    for i, ex in enumerate(examples):
        examples_text += f"Example {i+1}:\nTicket: {ex['ticket']}\nCategory: {ex['category']}\n\n"
    
    prompt = f"""You are an insurance support ticket classifier. 
Here are some examples of how to classify tickets:
{examples_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()

Result: Accuracy jumps to ~82%. The key is selecting diverse examples that cover edge cases and ambiguous scenarios.

Step 3: Adding Chain-of-Thought Reasoning

Chain-of-thought (CoT) prompting asks Claude to reason step-by-step before giving the final answer. This is particularly powerful for complex classification tasks.

def classify_ticket_cot(ticket_text: str, examples: list) -> str:
    examples_text = ""
    for i, ex in enumerate(examples):
        examples_text += f"Example {i+1}:\nTicket: {ex['ticket']}\nReasoning: {ex['reasoning']}\nCategory: {ex['category']}\n\n"
    
    prompt = f"""You are an insurance support ticket classifier. 
For each ticket, first reason step-by-step about which category fits best, then provide the category.
Here are some examples:
{examples_text}
Now classify this ticket:
Ticket: {ticket_text}
Reasoning:"""
    
    response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=200,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.content[0].text.strip()

Result: Accuracy reaches ~88%. The reasoning step helps Claude disambiguate between similar categories (e.g., "Billing Inquiries" vs. "Policy Administration" when a ticket mentions both charges and policy changes).

Step 4: Retrieval-Augmented Generation (RAG) for Dynamic Examples

Static few-shot examples have a limit. With RAG, we dynamically retrieve the most relevant examples for each ticket from a vector database. This is the game-changer.

Building the Vector Database

import voyageai
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
Initialize VoyageAI client
vo = voyageai.Client(api_key=os.environ.get("VOYAGE_API_KEY"))
Generate embeddings for your training data
def get_embeddings(texts: list) -> list:
    result = vo.embed(texts, model="voyage-2")
    return result.embeddings
Store embeddings with their labels
training_data = [
    {"ticket": "I need help with my premium payment...", "category": "Billing Inquiries"},
    # ... more training examples
]
ticket_texts = [item["ticket"] for item in training_data]
ticket_embeddings = get_embeddings(ticket_texts)

Retrieving Relevant Examples at Inference Time

def retrieve_similar_examples(query: str, k: int = 3) -> list:
    query_embedding = get_embeddings([query])[0]
    
    # Calculate cosine similarity
    similarities = cosine_similarity(
        [query_embedding], 
        ticket_embeddings
    )[0]
    
    # Get top-k indices
    top_indices = np.argsort(similarities)[-k:][::-1]
    
    return [training_data[i] for i in top_indices]
def classify_ticket_rag(ticket_text: str) -> str:
    # Dynamically retrieve relevant examples
    similar_examples = retrieve_similar_examples(ticket_text, k=3)
    
    # Use the few-shot prompt with retrieved examples
    return classify_ticket_cot(ticket_text, similar_examples)

Result: Accuracy soars to 95%+. By retrieving the most semantically similar examples for each query, Claude gets the most relevant context every time.

Step 5: Evaluation and Iteration

To measure your classifier's performance, use standard classification metrics:

from sklearn.metrics import accuracy_score, classification_report
Test your classifier on a held-out test set
test_tickets = ["...", "..."]  # Your test data
true_labels = ["...", "..."]   # Ground truth
predictions = []
for ticket in test_tickets:
    pred = classify_ticket_rag(ticket)
    predictions.append(pred)
Calculate accuracy
accuracy = accuracy_score(true_labels, predictions)
print(f"Accuracy: {accuracy:.2%}")
Get detailed metrics
print(classification_report(true_labels, predictions))

Best Practices for Production Deployments

• Start simple, iterate fast: Begin with zero-shot, then add few-shot examples, then CoT, then RAG. Each step should show measurable improvement.

• Curate your examples carefully: For RAG, quality matters more than quantity. 50-100 well-chosen examples often outperform 500 noisy ones.

• Handle edge cases explicitly: Add specific examples for ambiguous scenarios (e.g., a ticket about a billing error related to a claim).

• Monitor and log: Track classification confidence and flag low-confidence predictions for human review.

• Consider cost-performance tradeoffs: Claude 3 Sonnet is faster and cheaper than Opus, but Opus may be necessary for complex edge cases.

Key Takeaways

• Combine techniques for maximum accuracy: Zero-shot prompting alone achieves ~70% accuracy. Adding few-shot examples brings it to ~82%. Chain-of-thought reasoning pushes it to ~88%. RAG with dynamic example retrieval achieves 95%+.
• RAG is the game-changer: Dynamically retrieving the most relevant examples for each query dramatically outperforms static few-shot prompts.
• Explainability is built-in: Unlike traditional ML classifiers, Claude can explain its reasoning, making it suitable for regulated industries like insurance.
• Start small and iterate: You don't need thousands of training examples. A well-curated set of 50-100 examples combined with RAG can achieve production-grade accuracy.
• Chain-of-thought reasoning resolves ambiguity: Asking Claude to reason step-by-step before classifying helps disambiguate tickets that span multiple categories.