Google’s Newly Acquired Tool, Prompt Poet, Revolutionizes LLM Prompt Engineering

In the era of artificial intelligence, prompt engineering has emerged as a crucial skill for unlocking the immense potential of large language models (LLMs). This involves the intricate process of designing complex inputs to elicit relevant, useful outputs from AI models like ChatGPT. While many LLMs are user-friendly and respond well to conversational prompts, advanced prompt engineering techniques provide an additional layer of control. These techniques are not only beneficial for individual users but are indispensable for developers aiming to build sophisticated AI-powered applications.

Introducing the Game-Changer: Prompt Poet

Prompt Poet is an innovative tool developed by Character.ai, a platform and makerspace for personalized conversational AIs, which was recently acquired by Google. Prompt Poet potentially offers a glimpse into the future of prompt context management across Google’s AI projects, such as Gemini.

Prompt Poet stands out from other frameworks like Langchain due to its simplicity, focus, and several key advantages:

• Low Code Approach: Makes prompt design easier for both technical and non-technical users, unlike more code-intensive frameworks​.
• Template Flexibility: Utilizes YAML and Jinja2 to support complex prompt structures.
• Context Management: Seamlessly integrates external data, offering a more dynamic and data-rich prompt creation process.
• Efficiency: Minimizes time spent on engineering string manipulations, allowing users to concentrate on crafting optimal prompt text.

This article delves into the critical concept of context in prompt engineering, specifically the components of instructions and data. We’ll explore how Prompt Poet can streamline the creation of dynamic, data-rich prompts, enhancing the effectiveness of your LLM applications.

Understanding the Importance of Context: Instructions and Data

Customizing an LLM application often involves providing it with detailed instructions about its behavior. This could mean defining a personality type, a specific situation, or even emulating a historical figure. For instance:

When customizing an LLM application, such as a chatbot, you often need to give it specific instructions about its behavior. This could involve describing a certain personality type, situation, or role, or even a specific historical or fictional person. For example, when seeking help with a moral dilemma, you can ask the model to answer in the style of a specific person, which will significantly influence the type of answer you receive. Try variations of the following prompt to see how the details (like the people you choose) matter:

Simulate a panel discussion with the philosophers Aristotle, Karl Marx, and Peter Singer. Each should provide individual advice, comment on each other's responses, and conclude. Suppose they are very hungry.

The question: The pizza place gave us an extra pie, should I tell them or should we keep it?

Details are crucial. Effective prompt engineering also involves creating a specific, customized data context. This means providing the model with relevant facts, like personal user data, real-time information or specialized knowledge, which it wouldn’t have access to otherwise. This approach allows the AI to produce output far more relevant to the user’s specific situation than would be possible for an uninformed generic model.

Streamlining Data Management with Prompt Templating

Data can be manually inputted into ChatGPT. For instance, if you ask for advice about how to install some software, you have to tell it about your hardware. If you ask for help crafting the perfect resume, you have to tell it your skills and work history first. However, while this is fine for personal use, it does not work for development. Even for personal use, manually inputting data for each interaction can be tedious and prone to errors.

This is where prompt templating comes into play. Prompt Poet uses YAML and Jinja2 to create flexible and dynamic prompts, significantly enhancing LLM interactions.

Example: Crafting a Daily Planner

To demonstrate the power of Prompt Poet, let’s walk through a simple example: a daily planning assistant that will remind the user of upcoming events and provide contextual information to help prepare for their day, based on real-time data.

For instance, you might want output like this:

Good morning! It looks like you have virtual meetings in the morning and an afternoon hike planned. Don't forget water and sunscreen for your hike since it's sunny outside.  Here are your schedule and current conditions for today:  - **09:00 AM:** Virtual meeting with the marketing team  - **11:00 AM:** One-on-one with the project manager  - **07:00 PM:** Afternoon hike at Discovery Park with friends  It's currently 65°F and sunny. Expect good conditions for your hike. Be aware of a bridge closure on I-90, which might cause delays.

To achieve that, we’ll need to provide at least two different pieces of context to the model, 1) customized instructions about the task, and 2) the required data to define the factual context of the user interaction.

Prompt Poet provides us with powerful tools for handling this context. We’ll start by creating a template to hold the general form of the instructions, and filling it in with specific data at the time when we want to run the query. For the above example, we might use the following Python code to create a `raw_template` and the `template_data` to fill it, which are the components of a Prompt Poet `Prompt` object.

raw_template = """  - name: system instructions    role: system    content: |      You are a helpful daily planning assistant. Use the following information about the user's schedule and conditions in their area to provide a detailed summary of the day. Remind them of upcoming events and bring any warnings or unusual conditions to their attention, including weather, traffic, or air quality warnings. Ask if they have any follow-up questions.  - name: realtime data    role: system    content: |         Weather in {{ user_city }}, {{ user_country }}:      - Temperature: {{ user_temperature }}°C      - Description: {{ user_description }}      Traffic in {{ user_city }}:      - Status: {{ traffic_status }}      Air Quality in {{ user_city }}:      - AQI: {{ aqi }}      - Main Pollutant: {{ main_pollutant }}      Upcoming Events:      {% for event in events %}      - {{ event.start }}: {{ event.summary }}      {% endfor %}  """

The code below uses Prompt Poet’s `Prompt` class to populate data from multiple data sources into a template to form a single, coherent prompt. This allows us to invoke a daily planning assistant to provide personalized, context-aware responses. By pulling in weather data, traffic updates, AQI information and calendar events, the model can offer detailed summaries and reminders, enhancing the user experience.

You can clone and experiment with the full working code example, which also implements few-shot learning, a powerful prompt engineering technique that involves presenting the models with a small set of training examples.

# User data  user_weather_info = get_weather_info(user_city)  traffic_info = get_traffic_info(user_city)  aqi_info = get_aqi_info(user_city)  events_info = get_events_info(calendar_events)  template_data = {      "user_city": user_city,      "user_country": user_country,      "user_temperature": user_weather_info["temperature"],      "user_description": user_weather_info["description"],      "traffic_status": traffic_info,      "aqi": aqi_info["aqi"],      "main_pollutant": aqi_info["main_pollutant"],      "events": events_info  }  # Create the prompt using Prompt Poet  prompt = Prompt(      raw_template=raw_template_yaml,      template_data=template_data  )  # Get response from OpenAI  model_response = openai.ChatCompletion.create(    model="gpt-4",    messages=prompt.messages  )

Wrapping Up

Mastering the fundamentals of prompt engineering, particularly the roles of instructions and data, is crucial for maximizing the potential of LLMs. Prompt Poet stands out as a powerful tool in this field, offering a streamlined approach to creating dynamic, data-rich prompts.

Prompt Poet’s low-code, flexible template system makes prompt design accessible and efficient. By integrating external data sources that would not be available to an LLM’s training, data-filled prompt templates can better ensure AI responses are accurate and relevant to the user.

By leveraging tools like Prompt Poet, you can elevate your prompt engineering skills and develop innovative AI applications that meet diverse user needs with precision. As AI continues to evolve, staying proficient in the latest prompt engineering techniques will be essential.