prompt an LLM

def generate_personalized_description(product, customer_data):
    prompt = f"""
    Generate a product description for {product['name']} tailored for a customer with the following attributes:
    - Age: {customer_data['age']}
    - Interests: {', '.join(customer_data['interests'])}
    - Past purchases: {', '.join(customer_data['past_purchases'])}
    - Skill level: {customer_data['skill_level']}

    The description should highlight features that would appeal to this specific customer based on their profile.
    """

    response = llm.generate(prompt)
    return response.text

# Example usage
product = {
    "name": "AlpineX Hiking Boots",
    "features": ["waterproof", "lightweight", "durable"]
}

customer = {
    "age": 35,
    "interests": ["hiking", "photography"],
    "past_purchases": ["backpack", "trekking poles"],
    "skill_level": "intermediate"
}

personalized_description = generate_personalized_description(product, customer)
print(personalized_description)