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

Classification is one of the most common and impactful tasks in business automation. Whether you're routing support tickets, categorizing customer feedback, or tagging documents, getting classification right can save hours of manual work and improve customer satisfaction.

Traditional machine learning approaches to classification often struggle with complex business rules, limited training data, and the need for explainable results. Large Language Models (LLMs) like Claude have changed this landscape dramatically.

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

Why LLMs for Classification?

Before diving into the code, let's understand why LLMs are revolutionizing classification:

• Complex business rules: LLMs can understand nuanced, multi-layered classification criteria that would require extensive feature engineering in traditional ML
• Limited training data: LLMs perform well with few-shot examples, reducing the need for thousands of labeled samples
• Explainable results: Claude can provide natural language justifications for its classifications, making the system transparent and auditable
• Easy iteration: Changing classification criteria is as simple as updating a prompt, not retraining a model

Prerequisites

To follow along, you'll need:

• Python 3.11+ with basic familiarity
• An Anthropic API key (get one here)
• A VoyageAI API key (optional - embeddings can be pre-computed)
• Basic understanding of classification problems

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 clients:

import os
from anthropic import Anthropic
import voyageai
Load API keys from environment
anthropic_api_key = os.environ.get("ANTHROPIC_API_KEY")
voyage_api_key = os.environ.get("VOYAGE_API_KEY")
Initialize clients
client = Anthropic(api_key=anthropic_api_key)
vo = voyageai.Client(api_key=voyage_api_key)
Set model name
MODEL_NAME = "claude-3-opus-20240229"

Step 1: Define Your Classification Problem

For this guide, we'll build an Insurance Support Ticket Classifier. Insurance companies receive thousands of support tickets daily covering billing, claims, policy administration, and more. Manual categorization is slow and error-prone.

Here are the 10 categories we'll use:

• Billing Inquiries - Questions about invoices, charges, fees, and premiums
• Policy Administration - Requests for policy changes, updates, or cancellations
• Claims Assistance - Questions about the claims process and filing procedures
• Coverage Explanations - Questions about what is covered under specific policy types
• Account Management - Requests for account updates, password resets, or login issues
• Agent Support - Questions about agent commissions, training, or tools
• Fraud Reporting - Reports of suspicious activity or potential fraud
• Compliance Questions - Questions about regulatory requirements or legal issues
• Product Information - Requests for details about insurance products or services
• General Inquiries - Miscellaneous questions that don't fit other categories

Step 2: Start with a Simple Prompt (Baseline ~70% Accuracy)

Let's begin with a straightforward prompt that asks Claude to classify tickets. This gives us our baseline:

def classify_ticket_baseline(ticket_text, categories):
    """Simple classification without examples or reasoning."""
    prompt = f"""Classify the following insurance support ticket into one of these categories:
Categories:
{chr(10).join(f'{i+1}. {cat}' for i, cat in enumerate(categories))}
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: This approach typically achieves around 70% accuracy. It works for simple cases but struggles with ambiguous tickets or edge cases.

Step 3: Add Few-Shot Examples (Improve to ~85% Accuracy)

By providing a few labeled examples in the prompt, we can significantly improve accuracy:

def classify_ticket_few_shot(ticket_text, categories, examples):
    """Classification with few-shot examples."""
    examples_text = ""
    for example in examples:
        examples_text += f"Ticket: {example['text']}\nCategory: {example['category']}\n\n"
    
    prompt = f"""Classify the following insurance support ticket into one of these categories:
Categories:
{chr(10).join(f'{i+1}. {cat}' for i, cat in enumerate(categories))}
Here are some examples:
{examples_text}
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: With 3-5 well-chosen examples per category, accuracy jumps to approximately 85%. The key is selecting diverse examples that cover edge cases.

Step 4: Implement Retrieval-Augmented Generation (RAG) (Improve to ~90% Accuracy)

Instead of manually selecting examples, we can use a vector database to automatically retrieve the most relevant examples for each ticket. This is where RAG shines:

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
class TicketClassifier:
    def __init__(self, training_data, categories):
        self.categories = categories
        self.training_data = training_data
        self.embeddings = self._compute_embeddings(training_data)
    
    def _compute_embeddings(self, data):
        """Compute embeddings for all training examples."""
        texts = [item['text'] for item in data]
        result = vo.embed(texts, model="voyage-2")
        return np.array(result.embeddings)
    
    def _retrieve_similar_examples(self, query, k=3):
        """Retrieve k most similar examples from training data."""
        query_embedding = vo.embed([query], model="voyage-2").embeddings[0]
        similarities = cosine_similarity([query_embedding], self.embeddings)[0]
        top_indices = np.argsort(similarities)[-k:][::-1]
        return [self.training_data[i] for i in top_indices]
    
    def classify(self, ticket_text):
        """Classify a ticket using RAG."""
        similar_examples = self._retrieve_similar_examples(ticket_text)
        return classify_ticket_few_shot(ticket_text, self.categories, similar_examples)
Initialize classifier with training data
classifier = TicketClassifier(training_data, categories)
Classify a new ticket
result = classifier.classify("I need help understanding my premium increase for Q3")
print(f"Category: {result}")

Result: RAG-based retrieval pushes accuracy to approximately 90%. The system now dynamically finds the most relevant examples for each query.

Step 5: Add Chain-of-Thought Reasoning (Achieve 95%+ Accuracy)

The final improvement comes from asking Claude to reason step-by-step before giving its answer. This dramatically reduces errors on ambiguous cases:

def classify_ticket_cot(ticket_text, categories, examples):
    """Classification with chain-of-thought reasoning."""
    examples_text = ""
    for example in examples:
        examples_text += f"Ticket: {example['text']}\nCategory: {example['category']}\n\n"
    
    prompt = f"""You are an expert insurance support ticket classifier. Your task is to categorize tickets accurately.
Categories:
{chr(10).join(f'{i+1}. {cat}' for i, cat in enumerate(categories))}
Here are some examples:
{examples_text}
Ticket to classify: {ticket_text}
First, think step-by-step about what this ticket is asking:
What is the main topic or issue?
What specific action or information is being requested?
Which category best matches this request?

After your reasoning, provide your final answer in this format:
Category: [category name]
Confidence: [high/medium/low]
Reasoning: [brief explanation]"""
    
    response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=200,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.content[0].text.strip()

Result: Chain-of-thought reasoning combined with RAG achieves 95%+ accuracy. The system now handles edge cases and ambiguous queries with remarkable precision.

Testing and Evaluation

To properly evaluate your classifier, split your data into training and test sets:

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
Split data
X_train, X_test, y_train, y_test = train_test_split(
    tickets, labels, test_size=0.2, random_state=42
)
Evaluate
training_data = [{'text': t, 'category': c} for t, c in zip(X_train, y_train)]
classifier = TicketClassifier(training_data, categories)
predictions = []
for ticket in X_test:
    result = classifier.classify(ticket)
    predictions.append(result)
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.2%}")
print("\nClassification Report:")
print(classification_report(y_test, predictions))

Best Practices for Production

• Monitor confidence scores: Track when Claude expresses low confidence and route those cases for human review
• Regularly update examples: As new ticket types emerge, add them to your training data
• Use temperature 0: For classification tasks, set temperature to 0 for deterministic results
• Implement fallback logic: If Claude can't classify with high confidence, have a default category or escalation path
• Log everything: Keep records of classifications and reasoning for audit and improvement

Key Takeaways

• Start simple, then iterate: Begin with a basic prompt (70% accuracy), then add few-shot examples (85%), RAG (90%), and chain-of-thought reasoning (95%+) progressively
• RAG eliminates manual example selection: By using vector embeddings to retrieve relevant examples dynamically, you remove the need to hand-pick examples for every query
• Chain-of-thought reasoning is a game-changer: Asking Claude to explain its reasoning before giving an answer dramatically improves accuracy on ambiguous cases
• LLMs excel where traditional ML struggles: Complex business rules, limited training data, and the need for explainable results are all areas where Claude outperforms traditional classification approaches
• Production readiness requires monitoring: Always track confidence scores, implement fallback logic, and maintain a feedback loop for continuous improvement