Claude, a large language model from Anthropic, can be integrated into classification and routing pipelines to automate complex decision-making processes.
Let’s see this in action. Imagine a customer support scenario where incoming emails need to be categorized and then routed to the appropriate department.
import anthropic
client = anthropic.Anthropic(api_key="YOUR_ANTHROPIC_API_KEY")
def classify_and_route_email(email_body, subject):
message = client.messages.create(
model="claude-3-opus-20240229",
max_tokens=100,
temperature=0,
messages=[
{
"role": "user",
"content": f"""
Classify and route the following customer support email.
The categories are: 'Technical Support', 'Billing Inquiry', 'Sales Inquiry', 'General Feedback'.
The routing destinations are: 'engineering@example.com', 'accounts@example.com', 'sales@example.com', 'support@example.com'.
Subject: {subject}
Body: {email_body}
Respond in JSON format with 'category' and 'route_to' keys.
"""
}
]
)
return message.content[0].text
# Example Usage
email_subject = "My account is showing an incorrect balance"
email_body = "I noticed that my latest invoice from last month has a charge that I don't recognize. Can you please review my account and correct the balance?"
response_json_str = classify_and_route_email(email_body, email_subject)
print(response_json_str)
This code snippet demonstrates how Claude can analyze an email’s subject and body, determine its category, and suggest a routing address. The key is the prompt engineering: we explicitly define the possible categories and routing destinations, and instruct Claude to output the result in a structured JSON format, making it easy for downstream systems to parse and act upon.
The core problem Claude solves here is the ambiguity and variability in human language. Traditional keyword-based routing systems are brittle; they fail when users use synonyms, misspellings, or describe issues in novel ways. Claude, with its deep understanding of context and semantics, can interpret the intent behind the message, not just the literal words.
Internally, Claude processes the input text through its neural network, which has been trained on a massive dataset. This allows it to identify patterns, understand relationships between words and concepts, and infer the most probable classification and routing based on the provided context and the defined categories. The temperature parameter controls randomness; a value of 0 makes the output deterministic, which is crucial for consistent classification.
The primary lever you control is the prompt. By clearly defining the task, the desired output format (like JSON), and the scope of possible classifications and routes, you guide Claude’s behavior. You can also influence its reasoning by providing examples within the prompt (few-shot learning) or by adjusting the model parameters like max_tokens to control the length of its response, or temperature for creativity versus determinism.
Claude’s ability to handle nuanced language means it can often distinguish between closely related categories that simpler systems would struggle with. For instance, it can differentiate between a "Technical Support" issue related to a software bug and a "Billing Inquiry" about a charge for that same software. This fine-grained understanding is what enables more efficient and accurate routing, reducing the need for manual triage and improving customer satisfaction.
When constructing your prompts, remember that Claude’s interpretation is heavily influenced by the order and phrasing of your instructions. For complex pipelines, breaking down the task into sequential calls to Claude, where the output of one step becomes the input for the next, can yield more robust results than a single, monolithic prompt.
The next logical step after routing is to then use Claude to generate a personalized response to the customer based on the identified category and routing.