Enhancing Kedro Documentation With A Prompt Library For LLM Integration

Hey guys! We're super stoked to dive into an exciting new addition to the Kedro ecosystem – a Prompt Library right in our documentation! This is all about making it easier for you to use Large Language Models (LLMs) in your Kedro workflows. Think of it as a treasure trove of ready-to-go prompts that you can tweak and use in your projects. Let's break down what this means and why it's a game-changer.

What's the Big Idea?

The main goal here is to provide you with practical, hands-on examples that you can adapt to your specific needs. The Prompt Library will be a living, breathing resource that grows with community input and real-world usage. Instead of just telling you about best practices, we're going to show you how to apply them. This is especially cool because LLMs can take in both Kedro's guidelines and your project files, allowing for some seriously personalized advice.

Personalized Actionable Fixes

Imagine an LLM that can:

• Detect anti-patterns and misconfigurations in your project.
• Suggest corrections directly in your YAML, Python, or even hooks.
• Rewrite your code to align with Kedro standards.

That's the power we're aiming for! This isn't just generic advice; it's tailored, actionable guidance that helps you level up your Kedro projects.

Why a Prompt Library?

Kedro's documentation already does a solid job explaining best practices for things like securing credentials, versioning datasets, and modularizing pipelines. But the Prompt Library takes it a step further. It bridges the gap between theory and practice. It's like having a Kedro expert looking over your shoulder, offering suggestions based on your unique project.

LLMs: Your New Kedro Co-Pilot

LLMs have the unique ability to analyze two things at once: Kedro's rules and your project files. This opens up a world of possibilities. By leveraging this, we can create prompts that help you:

• Apply Best Practices: Ensure your project follows Kedro's recommended guidelines.
• Identify Anti-Patterns: Spot potential issues before they become problems.
• Get Personalized Fixes: Receive suggestions tailored to your specific codebase.

Initial Use Cases (MVP)

We're starting with a Minimum Viable Product (MVP) that focuses on key areas where LLMs can make a real difference. Here’s a sneak peek at some of the initial use cases we're targeting:

1. Apply Best Practices

This is all about making sure your project is in tip-top shape. Imagine prompts like:

• Data Catalog Audit: "Check my catalog.yml against Kedro best practices and suggest corrections." This prompt can help you ensure your data catalog is well-structured, efficient, and follows Kedro's recommended conventions. It’s like having a second pair of eyes to catch any potential misconfigurations or areas for improvement. By adhering to best practices, you can create a more maintainable and scalable data pipeline.

• Configuration Review: "Audit my conf/base and conf/local configs for clean separation and security." This prompt helps you maintain a clean separation between your base and local configurations, which is crucial for managing different environments and ensuring that sensitive information isn't exposed. It also checks for potential security vulnerabilities in your configurations, such as hardcoded credentials or insecure settings. Properly configured environments make your project robust and secure.

• Nodes and Pipelines Assessment: "Review my pipeline/nodes for structure, clarity, and Kedro standards." This prompt can analyze your pipelines and nodes to ensure they are well-structured, easy to understand, and adhere to Kedro's standards. It can help identify areas where you can improve the organization of your code, making it more readable and maintainable. A well-structured pipeline is easier to debug, modify, and scale as your project grows.

These prompts are designed to help you keep your Kedro project aligned with best practices, ensuring it’s robust, maintainable, and scalable. By automating these checks, you can focus on the core logic of your data pipelines rather than getting bogged down in configuration details.

2. Testing

Testing is crucial for ensuring the reliability of your Kedro projects. One of the initial prompts we're considering is:

• Unit Test Generation: "Write pytest tests for my nodes and pipelines." This prompt can help you generate unit tests for your nodes and pipelines, making it easier to ensure that your code behaves as expected. Writing tests can be a time-consuming task, but it’s essential for maintaining code quality and preventing bugs. This prompt automates the process, allowing you to focus on defining the test cases and ensuring comprehensive coverage of your project. Automated tests catch regressions early and provide confidence when making changes to your codebase.

By automating the creation of unit tests, you can improve the quality and reliability of your Kedro projects. This not only saves time but also helps ensure that your data pipelines are robust and less prone to errors.

3. Migration

Keeping up with the latest versions of Kedro is important for taking advantage of new features and improvements. We plan to include a prompt for:

• Version Migration: "Migrate my project from Kedro 0.18 to Kedro 0.19." or "Update datasets, settings, and hooks to the latest version." This prompt can assist you in migrating your project from an older version of Kedro to a newer one. Migrations can be complex, often involving changes to datasets, settings, and hooks. This prompt helps streamline the process by identifying the necessary updates and guiding you through the steps required to bring your project up to date. Staying current with Kedro ensures you can leverage the latest features and performance enhancements.

This type of prompt can significantly reduce the effort required to upgrade your Kedro projects, allowing you to take advantage of the latest features and improvements without spending hours manually updating your codebase.

4. Deployment

Deploying your Kedro projects can be challenging, especially when integrating with tools like Airflow. A prompt in this area could be:

• Deployment Best Practices: "Suggest how to configure my Kedro project to work with Airflow (DAG skeleton, configs, Docker)." This prompt can provide guidance on configuring your Kedro project for deployment with Airflow, including generating DAG skeletons, configuring settings, and setting up Docker containers. Deploying a Kedro project involves many steps, from setting up the environment to configuring the pipeline for execution. This prompt helps simplify the process by providing a clear roadmap and best practices for deployment, making it easier to integrate Kedro with orchestration tools like Airflow.

By automating the configuration process, you can streamline the deployment of your Kedro projects, making it easier to put your data pipelines into production.

5. Audit

Regularly auditing your Kedro projects can help identify potential issues and ensure they adhere to best practices. We envision a prompt like:

• Anti-Pattern Scan: "Scan my project for anti-patterns." or "Check configs, catalog, and pipelines against Kedro guidelines." This prompt can scan your project for anti-patterns and suggest improvements based on Kedro guidelines. Anti-patterns can lead to performance issues, maintenance challenges, and even bugs. This prompt acts as an automated code review, identifying potential problems in your configurations, catalog, and pipelines. By addressing these issues early, you can maintain a cleaner, more efficient, and more reliable Kedro project.

This prompt can help you identify and address potential issues in your Kedro projects, ensuring they remain robust and maintainable over time. Regular audits are essential for maintaining the health of your data pipelines.

6. Notebook Conversion

Many data science projects start in notebooks, but converting them to Kedro projects can be a hassle. A prompt for this use case could be:

• Notebook to Project Conversion: "Convert my notebook to a Kedro project." or "Transform this notebook into a Kedro project scaffold (v)." This prompt can help you convert your Jupyter notebooks into a Kedro project scaffold, including setting up the project structure, defining nodes and pipelines, and configuring datasets. Converting notebooks to Kedro projects can be a significant step in productionizing your data science work. This prompt streamlines the process by automating the setup of the project structure and helping you organize your code into Kedro's modular components. This makes it easier to manage, test, and deploy your data pipelines.

This prompt can significantly simplify the process of converting notebooks to Kedro projects, making it easier to transition your data science work into a production-ready environment.

The Result: Personalized, Actionable Fixes

The real magic of the Prompt Library is that it's not just about generic advice. It's about giving you personalized, actionable fixes. By analyzing your project's specific context, the LLM can provide suggestions that are tailored to your needs. This means you get:

• Targeted Recommendations: Suggestions that directly address your project's specific challenges.
• Practical Solutions: Concrete steps you can take to improve your project.
• Faster Development: Streamlined workflows and reduced debugging time.

Let's Build This Together!

This is just the beginning, guys! We're super excited to see how the Prompt Library evolves with your feedback and contributions. This is a community effort, and we're all in this together. So, stay tuned for more updates, and let's build something awesome!

By incorporating this Prompt Library into Kedro's documentation, we're not just providing another feature; we're empowering you to build better data pipelines with the help of cutting-edge AI. This is a big step towards making Kedro even more user-friendly and powerful. Let's revolutionize the way we build data pipelines together!