In this updated blog post, we’ll explore practical, programmatic implementations of priming techniques using Claude. We’ll provide Python code examples for each technique and then combine them in a real-world application.
1. One-Shot Learning: Sentiment Analysis
Let’s implement one-shot learning for sentiment analysis using Claude:
import anthropic
client = anthropic.Client("your-api-key")
def one_shot_sentiment_analysis(text):
prompt = f"""I want you to perform sentiment analysis. Here's an example:
Text: "I absolutely loved the new restaurant downtown!"
Sentiment: Positive
Now, analyze the sentiment of this text: "{text}"
Provide the sentiment as a single word: Positive, Negative, or Neutral."""
response = client.completion(
prompt=prompt,
model="claude-3-opus-20240229",
max_tokens_to_sample=100,
temperature=0
)
return response.completion.strip()
# Example usage
text = "The customer service was terrible, and I'll never shop there again."
sentiment = one_shot_sentiment_analysis(text)
print(f"Sentiment: {sentiment}")
This function uses one example to prime Claude for sentiment analysis, then analyzes the given text.
2. Multi-Shot Learning: Customer Inquiry Classification
Here’s an implementation of multi-shot learning for classifying customer inquiries:
import anthropic
client = anthropic.Client("your-api-key")
def multi_shot_inquiry_classification(inquiry):
prompt = f"""Classify customer inquiries. Here are a few examples:
1. "How do I reset my password?" - Category: Account Management
2. "When will my order arrive?" - Category: Order Status
3. "Can I return an item I bought last week?" - Category: Returns and Refunds
Now, classify this inquiry: "{inquiry}"
Provide the category as a single phrase, matching the format of the examples above."""
response = client.completion(
prompt=prompt,
model="claude-3-opus-20240229",
max_tokens_to_sample=100,
temperature=0
)
return response.completion.strip()
# Example usage
inquiry = "I received the wrong item in my package."
category = multi_shot_inquiry_classification(inquiry)
print(f"Category: {category}")
This function uses multiple examples to prime Claude for inquiry classification, improving its performance on various types of customer inquiries.
3. Zero-Shot Learning: News Article Classification
Let’s implement zero-shot learning for classifying news articles:
import anthropic
client = anthropic.Client("your-api-key")
def zero_shot_news_classification(article):
prompt = f"""Classify the following news article into one of these categories: "Technology", "Politics", "Environment", or "Sports". Use these guidelines:
- Technology: Articles about new inventions, digital trends, or scientific breakthroughs
- Politics: Stories about government actions, elections, or international relations
- Environment: News related to climate change, conservation, or natural disasters
- Sports: Reports on athletic events, player transfers, or sports regulations
Article: "{article}"
Provide the category as a single word from the list above."""
response = client.completion(
prompt=prompt,
model="claude-3-opus-20240229",
max_tokens_to_sample=100,
temperature=0
)
return response.completion.strip()
# Example usage
article = "Scientists have discovered a new method to remove microplastics from the ocean using magnetic coils, potentially solving a major environmental crisis."
category = zero_shot_news_classification(article)
print(f"Category: {category}")
This function uses category descriptions to prime Claude for zero-shot classification of news articles.
4. Chain of Thought: Solving Complex Business Problems
Here’s an implementation of chain of thought for solving complex business problems:
import anthropic
client = anthropic.Client("your-api-key")
def chain_of_thought_problem_solving(problem):
prompt = f"""Solve this business problem using a step-by-step approach. Show your work and calculations for each step:
{problem}
Provide your solution in a structured format with numbered steps."""
response = client.completion(
prompt=prompt,
model="claude-3-opus-20240229",
max_tokens_to_sample=1000,
temperature=0
)
return response.completion.strip()
# Example usage
problem = """A small business sells custom t-shirts. They buy plain shirts for $5 each and spend $2 on materials and $3 on labor to customize each shirt. They sell the shirts for $25. If they sell 100 shirts a month, what is their monthly profit after deducting 20% for taxes?"""
solution = chain_of_thought_problem_solving(problem)
print(f"Solution:n{solution}")
This function encourages Claude to break down complex problems into steps, showing the chain of thought in problem-solving.
5. Combining Techniques: Customer Support Pipeline
Now, let’s create a customer support pipeline that combines all these techniques:
import anthropic
client = anthropic.Client("your-api-key")
def customer_support_pipeline(query):
# Step 1: Zero-Shot Classification
classification_prompt = f"""Classify the following customer query into one of these categories: "Billing Issue", "Account Management", "Technical Support", or "Product Inquiry".
Query: "{query}"
Provide the category as a single phrase from the list above."""
classification = client.completion(
prompt=classification_prompt,
model="claude-3-opus-20240229",
max_tokens_to_sample=100,
temperature=0
).completion.strip()
# Step 2: One-Shot Sentiment Analysis
sentiment_prompt = f"""Perform sentiment analysis. Here's an example:
Text: "I love how easy it is to use your product!"
Sentiment: Positive
Now, analyze the sentiment of this query: "{query}"
Provide the sentiment as a single word: Positive, Negative, or Neutral."""
sentiment = client.completion(
prompt=sentiment_prompt,
model="claude-3-opus-20240229",
max_tokens_to_sample=100,
temperature=0
).completion.strip()
# Step 3: Multi-Shot Response Generation
response_prompt = f"""Generate an appropriate customer service response. Here are a few examples:
1. Billing Issue: "I apologize for the inconvenience. I'll look into the charge right away and assist you with any necessary adjustments."
2. Account Management: "I'd be happy to help you manage your account. Could you please provide more details about what you'd like to do?"
3. Technical Support: "I'm sorry you're having trouble. Let's troubleshoot this step-by-step to resolve your issue quickly."
Now, generate a response for this query: "{query}"
Take into account that the query was classified as {classification} and the sentiment was {sentiment}.
Provide a response that addresses the customer's concerns."""
response = client.completion(
prompt=response_prompt,
model="claude-3-opus-20240229",
max_tokens_to_sample=200,
temperature=0.7
).completion.strip()
# Step 4: Chain of Thought for Complex Queries
if "complex" in classification.lower() or len(query.split()) > 20:
cot_prompt = f"""The following customer query requires a detailed solution. Break down the problem-solving process into steps:
Query: "{query}"
Provide a step-by-step solution, explaining the reasoning at each stage."""
cot_solution = client.completion(
prompt=cot_prompt,
model="claude-3-opus-20240229",
max_tokens_to_sample=500,
temperature=0
).completion.strip()
else:
cot_solution = "N/A"
return {
"Classification": classification,
"Sentiment": sentiment,
"Generated Response": response,
"Detailed Solution": cot_solution
}
# Example usage
query = "I've been charged twice for my subscription renewal, and I can't figure out how to cancel it. This is frustrating!"
result = customer_support_pipeline(query)
print("Customer Support Pipeline Result:")
for key, value in result.items():
print(f"{key}: {value}n")
This comprehensive example demonstrates how to combine all four priming techniques in a real-world customer support application. It classifies the query, analyzes sentiment, generates an appropriate response, and provides a detailed solution for complex queries.
Conclusion
These programmatic examples showcase how to implement various priming techniques with Claude in real-world scenarios. By leveraging these techniques, developers can create sophisticated AI-powered applications that adapt to different tasks and provide context-aware responses. As you build your own applications, consider how these priming techniques can be combined and customized to suit your specific use cases and improve overall performance.
