Revolutionizing LLM Prompt Engineering: The Power of Prompt Poet

Prompt engineering is a crucial skill for harnessing the full potential of large language models (LLMs). It involves crafting complex inputs to extract relevant, useful outputs from AI models like ChatGPT. While many LLMs are designed to be friendly to non-technical users, advanced prompt engineering techniques offer another powerful level of control. These techniques are useful for individual users and absolutely essential for developers seeking to build sophisticated AI-powered applications.

The Game-Changer: Prompt Poet

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

Key Features of Prompt Poet

• Low Code Approach: Simplifies prompt design for both technical and non-technical users, unlike more code-intensive frameworks.
• Template Flexibility: Uses 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: Reduces time spent on engineering string manipulations, allowing users to focus on crafting optimal prompt text.

The Importance of Context: Instructions and Data

Customizing an LLM application often involves giving it detailed instructions about how to behave. This might mean defining a personality type, a specific situation, or even emulating a historical figure.

For instance, customizing an LLM application, such as a chatbot, often means giving it specific instructions about how to act. This might mean describing a certain type of personality type, situation, or role, or even a specific historical or fictional person. For example, when asking for help with a moral dilemma, you can ask the model to answer in the style of someone specific, which will very much influence the type of answer you get.

Try variations of the following prompt to see how the details (like the people you pick) 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?

Philosophers discussing

Efficient Data Management with Prompt Templating

Data can be loaded in manually, just by typing it into ChatGPT. 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 ok for personal use, it does not work for development.

Even for personal use, manually inputting data for each interaction can be tedious and error-prone.

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: Daily Planner

To illustrate the power of Prompt Poet, let’s work 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.

Daily planner

For example, 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 do 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 gives us some 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.

Conclusion

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 using 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.

AI applications