Prompt engineering, the discipline of crafting just the correct input to a large language model (LLM) to get the desired response, is a critical new skill for the age of AI. It’s helpful for even casual users of conversational AI but essential for builders of the next generation of AI-powered applications.
Enter Prompt Poet, the brainchild of Character.ai, a conversational LLM startup recently acquired by Google. Prompt Poet simplifies advanced prompt engineering by offering a user-friendly, low-code template system that effectively manages context and seamlessly integrates external data. This allows you to ground LLM-generated responses in a real-world data context, opening up a new horizon of AI interactions.
Prompt Poet shines for its seamless integration of “few-shot learning,” a powerful technique for rapid customization of LLMs without requiring complex and expensive model fine-tuning. This article explores how few-shot learning with Prompt Poet can be leveraged to deliver bespoke AI-driven interactions with ease and efficiency.
Could Prompt Poet be a glimpse into Google’s future approach to prompt engineering across Gemini and other AI products? This exciting potential is worth a closer look.
The Power of Few-Shot Learning
In few-shot learning, we give the AI a handful of examples illustrating the responses we want for possible prompts and a few ‘shots’ of how it should behave in similar scenarios.
The beauty of few-shot learning is its efficiency. Model fine-tuning involves retraining a model on a new dataset, which can be computationally intensive, time-consuming, and costly, especially when working with large models. On the other hand, few-shot learning provides a small set of examples with the prompt to adjust the model’s behavior to a specific context. Even fine-tuned models can benefit from few-shot learning to tailor their behavior to a more specific context.
How Prompt Poet Makes Few-Shot Learning Accessible
Prompt Poet shines in its ability to simplify the implementation of few-shot learning. Using YAML and Jinja2 templates, Prompt Poet allows you to create complex, dynamic prompts that directly incorporate few-shot examples into the prompt structure.
Suppose you want to develop a customer service chatbot for a retail business to explore an example. Using Prompt Poet, you can easily include customer information such as order history, the status of any current orders, and information about current promotions and sales.
But what about tone? Should it be more friendly and funny or formal? More concise or informative? By including a “few shots” of successful examples, you can fine-tune the chatbot’s responses to match the distinct voice of each brand.
Base Instruction
The base instruction for the chatbot might be:
– name: system instructions
role: system
content: |
You are a customer service chatbot for a retail site. Your job is to assist customers by answering their questions, providing helpful information, and resolving issues. Below, you will be provided some example user inputs paired with desirable responses regarding tone, style, and voice. Emulate these examples in your responses to the user.
In these examples, placeholders marked with double question marks like ‘??placeholder??’ will be used instead of actual user data. After the examples, you’ll be provided with accurate data about the user’s current and past orders as a customer, which you must use faithfully in dealing with the user.
We can also provide the model with accurate customer data to accurately answer questions about the user’s past and current orders. If appropriate, we can also include current sales and promotions.
– name: customer data
role: system
content: |
current orders:
{% for order in current_orders %}
– {{ order.description }}
{% endfor %}
Past orders:
{% for order in past_orders %}
– {{ order.description }}
{% endfor %}
– name: promotions
role: system
content: |
promotions:
{% for promotion in promotions %}
– {{ promotion.description }}
{% endfor %}
But first, we need to provide examples of the style, tone, and voice we promised the model in the base instruction.
Are you setting the tone to be friendly or formal?
Some brands have a more casual, friendly, and jocular tone, whereas others prefer a more refined elegance.
For a brand that has a friendly and casual tone, the few-shot learning examples could look like this:
– name: user prompt
role: user
content: |
Hi, I ordered ??product_name. But I haven’t received it yet. What’s going on?
– name: response
role: assistant
content: |
Hey,??user_name??! Sorry for the hassle—let’s get this sorted out ASAP. I’m pulling up your order for ??product_name. It was placed on ??order_date??. Hang tight while I check where it’s at!
– name: user prompt
role: user
content: |
I need to update my shipping address for an order I made today; it should be ??new_address.
– name: response
role: assistant
content: |
Gotcha, ??user_name??! Let’s make sure your ??product_name?? Gets to the right place. I’ll switch your shipping to ??new_address. Right away. All set—is there anything else I can do for you?
– name: user prompt
role: user
content: |
What’s your return policy? I’m not sure I’m going to love product_name.
– name: response
role: assistant
content: |
No worries, ??user_name??! If ??product_name?? doesn’t knock your socks off, we’ve got a super easy return process. Just let me know, and I’ll help you sort it out. ?
For a brand that emphasizes a more formal and professional tone, different answers can be provided to the very same prompts, guiding the model toward a different style:
– name: user prompt
role: user
content: |
Hi, I ordered ??product_name. But I haven’t received it yet. Can you assist?
– name: response
role: assistant
content: |
Thank you for reaching out,?? user_name?? I apologize for any inconvenience this may have caused. I will promptly review the status of your order for?? product_name, placed on?? order_date??, and I will provide an update shortly.
– name: user prompt
role: user
content: |
I need to update my shipping address for an order I made today; it should be ??new_address.
– name: response
role: assistant
content: |
Certainly, ??user_name??. I will update your shipping information to ??new_address?? for today’s order. The changes have been successfully applied. Is there anything else I can assist you with?
– name: user prompt
role: user
content: |
Could you explain your return policy? Do I need clarification about keeping ??product_name??.
– name: response
role: assistant
content: |
Certainly, ??user_name??. If ??product_name?? You may initiate a return within our specified timeframe if it does not meet your expectations. I will guide you through the process to ensure it is handled smoothly and efficiently.
Bringing it together
The code below uses Prompt Poet’s `Prompt` class to form a single, coherent prompt from the separate elements of the base instructions, the few-shot learning examples, and the actual user data. This allows us to invoke AI responses that are accurately informed and highly crafted in purpose and style.
# User data
user_past_orders = get_past_orders(user)
user_current_orders = get_current_orders(user)
promotions = get_promotions(user)
template_data = {
“past_orders”: user_past_orders,
“current_orders”: user_current_orders,
“promotions”: promotions
}
# Create the prompt using Prompt Poet
combined_template = base_instructions + few_shot_examples + customer_data
prompt = Prompt(
raw_template=combined_template,
template_data=template_data
)
# Get a response from OpenAI
model_response = open.ChatCompletion.create(
model= “GPT-4”,
messages=prompt.messages
)
Elevating AI with Prompt Poet
Prompt Poet is more than just a tool for managing context in AI prompts—it’s a gateway to advanced prompt engineering techniques like few-shot learning. By making it easy to compose complex prompts with accurate data and the voice-customizing power of few-shot examples, Prompt Poet empowers you to create sophisticated AI applications that are informative and customized to your brand.
As AI evolves, mastering techniques like few-shot learning will be crucial for staying ahead of the curve. Prompt Poet can help you harness the full potential of LLMs, creating powerful and practical solutions.