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

Classification is one of the most common and impactful applications of AI in business. Whether you're routing support tickets, categorizing documents, or flagging compliance issues, getting classification right can save thousands of hours and dramatically improve customer experience.

Traditional machine learning approaches to classification often struggle with complex business rules, limited training data, and the need for explainable results. This is where Large Language Models (LLMs) like Claude shine. In this guide, you'll learn how to build a production-ready classification system that achieves 95%+ accuracy by combining three powerful techniques: prompt engineering, retrieval-augmented generation (RAG), and chain-of-thought reasoning.

Why LLMs for Classification?

Before diving into the implementation, let's understand why LLMs have revolutionized classification tasks:

• Complex Business Rules: LLMs can understand nuanced, multi-layered classification criteria that would require extensive feature engineering in traditional ML
• Limited Training Data: Unlike traditional classifiers that need thousands of examples, LLMs can perform well with just dozens of labeled samples
• Explainable Results: Claude can provide natural language explanations for its classifications, making the system transparent and auditable
• Flexibility: You can update classification criteria by simply modifying prompts, without retraining models

Problem Definition: Insurance Support Ticket Classifier

For this guide, we'll build a system that classifies insurance support tickets into 10 categories. This is a perfect example of a real-world classification problem with complex business rules and varying data quality.

Category Definitions

• Billing Inquiries - Questions about invoices, charges, fees, premiums, payment methods
• Policy Administration - Policy changes, renewals, cancellations, coverage adjustments
• Claims Assistance - Claims process, documentation, status inquiries
• Coverage Explanations - What's covered, limits, exclusions, deductibles
• Account Management - Login issues, profile updates, contact information changes
• Product Information - Policy types, features, benefits, riders
• Agent Support - Agent-related inquiries, commissions, licensing
• Compliance & Regulatory - Legal questions, regulatory requirements, disclosures
• Technical Support - Website issues, mobile app problems, system access
• General Inquiries - Miscellaneous questions not fitting other categories

Prerequisites

Before starting, ensure you have:

• Python 3.11+ installed
• An Anthropic API key (get one here)
• Basic familiarity with Python and API usage
• Understanding of classification concepts

Step 1: Setup and Installation

First, install the required packages:

pip install anthropic voyageai pandas matplotlib scikit-learn numpy

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

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

Step 2: Data Preparation

Proper data preparation is crucial. You'll need:

• Training data: Labeled examples to guide the model
• Test data: Unseen examples for evaluation

import pandas as pd

Load your training and test data
train_df = pd.read_csv('insurance_tickets_train.csv')
test_df = pd.read_csv('insurance_tickets_test.csv')
print(f"Training samples: {len(train_df)}")
print(f"Test samples: {len(test_df)}")
print(f"Categories: {train_df['category'].unique()}")

Step 3: Prompt Engineering for Classification

The heart of your classification system is the prompt. Here's a template that achieves high accuracy:

def create_classification_prompt(query, category_definitions, examples=None):
    """
    Create a prompt for Claude to classify a support ticket.
    """
    prompt = f"""You are an expert insurance support ticket classifier. Your task is to classify the following support ticket into exactly one of the categories below.
CATEGORIES:
{category_definitions}
"""
    
    if examples:
        prompt += "RELEVANT EXAMPLES:\n"
        for i, example in enumerate(examples, 1):
            prompt += f"{i}. Ticket: {example['text']}\n   Category: {example['category']}\n\n"
    
    prompt += f"""TICKET TO CLASSIFY:
{query}
First, think step-by-step about which category best fits this ticket. Consider the specific details, keywords, and intent of the inquiry. Then provide your final classification.
Classification:"""
    
    return prompt

Why Chain-of-Thought Matters

By asking Claude to "think step-by-step" before providing the classification, you leverage chain-of-thought reasoning. This dramatically improves accuracy because:

• The model processes the query more thoroughly
• It considers multiple aspects before deciding
• The reasoning provides an audit trail for the classification

Step 4: Implementing Retrieval-Augmented Generation (RAG)

To boost accuracy further, we'll implement RAG to provide Claude with the most relevant examples from our training data.

import voyageai
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
Initialize VoyageAI for embeddings
vo = voyageai.Client(api_key=os.environ.get("VOYAGE_API_KEY"))
Create embeddings for training data
def create_embeddings(texts):
    """Create embeddings for a list of texts."""
    result = vo.embed(texts, model="voyage-2", input_type="document")
    return result.embeddings
Build embedding index
train_embeddings = create_embeddings(train_df['text'].tolist())
Function to retrieve similar examples
def retrieve_similar_examples(query, k=3):
    """
    Retrieve the k most similar examples from training data.
    """
    # Embed the query
    query_embedding = vo.embed([query], model="voyage-2", input_type="query").embeddings[0]
    
    # Calculate similarities
    similarities = cosine_similarity([query_embedding], train_embeddings)[0]
    
    # Get top k indices
    top_k_indices = np.argsort(similarities)[-k:][::-1]
    
    # Return similar examples
    similar_examples = []
    for idx in top_k_indices:
        similar_examples.append({
            'text': train_df.iloc[idx]['text'],
            'category': train_df.iloc[idx]['category']
        })
    
    return similar_examples

Step 5: Building the Classification Function

Now let's combine everything into a single classification function:

def classify_ticket(ticket_text, use_rag=True, k=3):
    """
    Classify an insurance support ticket using Claude.
    """
    # Retrieve similar examples if using RAG
    examples = None
    if use_rag:
        examples = retrieve_similar_examples(ticket_text, k=k)
    
    # Create the prompt
    prompt = create_classification_prompt(
        query=ticket_text,
        category_definitions=CATEGORY_DEFINITIONS,
        examples=examples
    )
    
    # Get classification from Claude
    response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=150,
        messages=[{"role": "user", "content": prompt}]
    )
    
    # Parse the response
    classification = response.content[0].text.strip()
    
    return classification

Step 6: Testing and Evaluation

Let's evaluate our system's performance:

def evaluate_classifier(test_data, use_rag=True):
    """
    Evaluate the classifier on test data.
    """
    correct = 0
    total = len(test_data)
    
    for idx, row in test_data.iterrows():
        predicted = classify_ticket(row['text'], use_rag=use_rag)
        actual = row['category']
        
        if predicted == actual:
            correct += 1
        
        print(f"Ticket {idx+1}: Predicted={predicted}, Actual={actual}")
    
    accuracy = correct / total * 100
    print(f"\nAccuracy: {accuracy:.2f}%")
    return accuracy
Test without RAG
print("Testing without RAG...")
accuracy_baseline = evaluate_classifier(test_df, use_rag=False)
Test with RAG
print("\nTesting with RAG...")
accuracy_rag = evaluate_classifier(test_df, use_rag=True)

Results and Optimization

Based on the original Anthropic cookbook, here's what you can expect:

• Baseline (no RAG): ~70% accuracy
• With RAG (3 examples): ~85% accuracy
• With RAG + Chain-of-Thought: ~90% accuracy
• With RAG + CoT + Optimized Prompting: 95%+ accuracy

Optimization Tips

• Increase K for RAG: Try 5-7 examples instead of 3 for better context
• Refine Category Definitions: Make them more specific and include examples
• Add Few-Shot Examples: Include 2-3 perfect examples per category in the prompt
• Use Temperature 0: For deterministic classification results
• Implement Confidence Thresholds: Flag low-confidence classifications for human review

# Production-ready classification with confidence
classify_ticket(
    ticket_text="I need help understanding my deductible for collision coverage",
    use_rag=True,
    k=5
)
Returns: "Coverage Explanations"

Key Takeaways

• LLMs excel at complex classification: Claude can handle nuanced business rules and limited training data that would challenge traditional ML approaches
• RAG dramatically improves accuracy: By providing relevant examples from your training data, you can boost accuracy from 70% to 85%+ without retraining
• Chain-of-thought reasoning adds value: Asking Claude to think step-by-step before classifying improves both accuracy and explainability
• Prompt engineering is iterative: Start with a baseline, test, and refine your prompts based on error analysis
• Production systems need confidence thresholds: Implement mechanisms to flag uncertain classifications for human review, ensuring reliability in critical applications

Next Steps

Now that you have a working classification system, consider:

• Adding a confidence scoring mechanism using Claude's log probabilities
• Implementing a feedback loop where corrections improve future classifications
• Extending the system to handle multi-label classification
• Building a simple web interface for non-technical users

Remember, the key to high-accuracy classification with Claude is combining the right techniques: clear prompt engineering, relevant context through RAG, and structured reasoning through chain-of-thought. Start simple, measure your results, and iterate based on where your system makes mistakes.