Building a High-Accuracy Classification System with Claude: From 70% to 95%+ Accuracy

Classification is one of the most common and impactful applications of large language models (LLMs). Whether you're routing customer support tickets, moderating content, or categorizing documents, getting classification right can dramatically improve operational efficiency.

In this guide, you'll learn how to build a production-ready classification system using Claude that achieves over 95% accuracy. We'll use a real-world example: classifying insurance support tickets into 10 distinct categories. You'll see how to combine prompt engineering, retrieval-augmented generation (RAG), and chain-of-thought reasoning to progressively improve your results.

Prerequisites

Before diving in, make sure you have:

• Python 3.11+ installed
• An Anthropic API key
• Basic familiarity with Python and API calls
• Understanding of classification problems

The Challenge: Insurance Support Ticket Classification

Insurance companies receive thousands of support tickets daily covering billing, claims, policy administration, and more. Manually categorizing these tickets is slow, expensive, and error-prone.

Our goal is to build a system that automatically classifies tickets into categories like:

• Billing Inquiries
• Policy Administration
• Claims Assistance
• Coverage Explanations
• And 6 more categories

Traditional machine learning approaches struggle here because:

• Business rules are complex and nuanced
• Training data is often limited or low-quality
• Categories may overlap or change over time

Claude excels in exactly these scenarios.

Step 1: 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
client = Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
Set your model
MODEL_NAME = "claude-3-opus-20240229"

Step 2: Preparing Your Data

Proper data preparation is crucial. You'll need:

• Training data: Examples with known categories
• Test data: Unseen examples for evaluation

Here's how to structure your data:

# Example training data structure
training_data = [
    {
        "text": "I was charged twice for my premium this month. Please refund the duplicate payment.",
        "category": "Billing Inquiries"
    },
    {
        "text": "I need to add my new car to my auto insurance policy.",
        "category": "Policy Administration"
    },
    # ... more examples
]

Step 3: Basic Prompt Engineering

Start with a simple prompt that defines the task clearly:

def classify_ticket(text, categories):
    prompt = f"""You are an insurance support ticket classifier.
    Classify the following ticket into exactly one of these categories:
    {', '.join(categories)}
Ticket: {text}
Category:"""
    
    response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=100,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.content[0].text.strip()

This basic approach typically achieves around 70% accuracy. Let's improve it.

Step 4: Adding Category Definitions and Examples

To boost accuracy, provide detailed definitions and examples for each category:

def create_enhanced_prompt(text, category_definitions):
    prompt = f"""You are an expert insurance support ticket classifier.
Category Definitions:
    {category_definitions}
Instructions:
    1. Read the ticket carefully
    2. Match it to the most appropriate category
    3. Output ONLY the category name
Ticket: {text}
Category:"""
    return prompt

With detailed definitions, accuracy typically jumps to 80-85%.

Step 5: Implementing Retrieval-Augmented Generation (RAG)

RAG dramatically improves accuracy by providing relevant examples from your training data. Here's how to implement it:

import voyageai
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
Initialize VoyageAI for embeddings
vo = voyageai.Client(api_key=os.environ["VOYAGE_API_KEY"])
Create embeddings for your training data
def create_embeddings(texts):
    result = vo.embed(texts, model="voyage-2")
    return result.embeddings
Find similar examples
def find_similar_examples(query, training_data, k=3):
    query_embedding = create_embeddings([query])[0]
    
    similarities = []
    for example in training_data:
        sim = cosine_similarity([query_embedding], [example["embedding"]])[0][0]
        similarities.append(sim)
    
    # Get top-k most similar examples
    top_indices = np.argsort(similarities)[-k:][::-1]
    return [training_data[i] for i in top_indices]

Now integrate RAG into your classification prompt:

def classify_with_rag(text, training_data, category_definitions):
    # Find similar examples
    similar_examples = find_similar_examples(text, training_data, 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"
    
    prompt = f"""You are an expert insurance support ticket classifier.
Category Definitions:
    {category_definitions}
Here are some similar tickets and their correct categories:
    {examples_text}
Now classify this ticket:
    Ticket: {text}
Category:"""
    
    response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=100,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.content[0].text.strip()

With RAG, accuracy typically reaches 90-95%.

Step 6: Adding Chain-of-Thought Reasoning

For the final accuracy boost, add chain-of-thought reasoning:

def classify_with_cot(text, training_data, category_definitions):
    similar_examples = find_similar_examples(text, training_data, k=3)
    
    examples_text = ""
    for i, ex in enumerate(similar_examples, 1):
        examples_text += f"Example {i}:\nTicket: {ex['text']}\nCategory: {ex['category']}\n\n"
    
    prompt = f"""You are an expert insurance support ticket classifier.
Category Definitions:
    {category_definitions}
Here are some similar tickets and their correct categories:
    {examples_text}
Now classify this ticket. First, think step by step about which category fits best.
    Then provide your final answer as: Category: [category_name]
Ticket: {text}
Reasoning:"""
    
    response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=300,
        messages=[{"role": "user", "content": prompt}]
    )
    
    # Parse the response to extract the category
    full_response = response.content[0].text.strip()
    # Extract category after "Category:"
    if "Category:" in full_response:
        return full_response.split("Category:")[-1].strip()
    return full_response

Chain-of-thought reasoning pushes accuracy to 95%+ by making the model's decision process transparent and more deliberate.

Step 7: Testing and Evaluation

Finally, evaluate your system systematically:

from sklearn.metrics import accuracy_score, classification_report
def evaluate_classifier(classifier_fn, test_data):
    predictions = []
    actual = []
    
    for item in test_data:
        pred = classifier_fn(item["text"])
        predictions.append(pred)
        actual.append(item["category"])
    
    accuracy = accuracy_score(actual, predictions)
    report = classification_report(actual, predictions)
    
    return accuracy, report
Run evaluation
accuracy, report = evaluate_classifier(classify_with_cot, test_data)
print(f"Accuracy: {accuracy:.2%}")
print("Classification Report:")
print(report)

Best Practices for Production

• Monitor accuracy over time: Categories and language evolve. Regularly retest your system.
• Handle edge cases: Add explicit instructions for ambiguous tickets (e.g., "If uncertain, choose 'Other'")
• Cache embeddings: Store embeddings to avoid recomputing them for every query.
• Use temperature 0: For classification, deterministic outputs are usually preferred.
• Log everything: Track predictions, confidence scores, and reasoning for audit trails.

Key Takeaways

• Start simple, then layer complexity: Begin with basic prompts (70% accuracy), add category definitions (80-85%), implement RAG (90-95%), and finish with chain-of-thought reasoning (95%+).
• RAG is a game-changer: Providing similar examples from your training data dramatically improves accuracy without requiring model fine-tuning.
• Chain-of-thought reasoning boosts performance: Asking Claude to reason step-by-step before outputting a classification leads to more accurate and explainable results.
• LLMs excel where traditional ML struggles: Complex business rules, limited training data, and overlapping categories are handled naturally by Claude.
• Evaluation is essential: Always measure accuracy with a held-out test set and use classification reports to identify weak categories.