Engineer your Prompt Library: Marimo Notebooks with o1-mini, Claude, Gemini

Introduction

This tutorial will guide you through the process of engineering a prompt library using Marimo Notebooks in conjunction with various AI models like o1-mini, Claude, and Gemini. By leveraging Marimo's features, you can create reusable, interactive prompts that enhance your efficiency and effectiveness in working with generative AI.

Step 1: Getting Started with Marimo Notebooks

• Visit the Marimo website at marimo.io to familiarize yourself with the platform.
• Download and install Marimo Notebooks to your local machine or set it up in your preferred environment.
• Explore the interface and basic functionalities, recognizing how it differs from traditional Jupyter Notebooks.

Step 2: Creating a New Prompt Notebook

• Open Marimo and create a new notebook.
• Use the interactive features to design your first prompt:
  • Define the purpose of the prompt clearly.
  • Input relevant variables that the prompt will utilize.
• Save your notebook to ensure your progress is not lost.

Step 3: Building Reusable Prompt Templates

• Start by developing reusable templates for common tasks:
  • Identify frequent tasks or queries for AI models.
  • Create a template that includes placeholders for variables.
• For example, a prompt template could look like this:

  prompt_template = "What is the impact of {topic} on {field}?"
  

• Save these templates within your Marimo library for easy access.

Step 4: Iterating on Your Prompts

• Test your prompt templates by executing them with different input values.
• Analyze the output and refine your prompts based on the results:
  • Adjust wording for clarity or specificity.
  • Experiment with different variable values to see how outputs change.
• Document your findings within the Marimo notebook to track your iterations.

Step 5: Utilizing Multiple AI Models

• Leverage Marimo’s capability to run prompts across various AI models:
  • Set up your notebook to include calls to o1-mini, Claude, and Gemini.
• Example code to execute a prompt with a chosen model:

  response = model.run(prompt_template.format(topic="AI", field="healthcare"))
  print(response)
  

• Compare outputs from different models to determine which best suits your needs.

Step 6: Version Control and Maintenance

• Use Marimo’s version control features to track changes to your prompt library.
• Regularly update your templates and prompts based on new findings or model updates.
• Organize your notebooks into categories for efficient navigation.

Conclusion

By following these steps, you can create a powerful and scalable prompt library using Marimo Notebooks. This library will not only enhance your productivity but also improve your interactions with generative AI models. As you become more comfortable with Marimo, consider exploring advanced features such as interactive data visualization and rapid prototyping to further expand your capabilities in AI coding. Embrace the iterative process and keep refining your prompts to stay at the forefront of AI engineering.