At a Glance

Struggling to find the right LLM model for each unique app and use case in an always changing landscape of LLMs? Learn to master model selection using Promptfoo. In this hands-on lab, you’ll learn to adapt to new models, handle pricing changes effectively, and perform regression testing. Through practical scenarios, you’ll gain the know-how to continuously choose the optimal LLM models for your projects. By the end, you’ll confidently navigate the evolving landscape of LLMs, keeping your app efficient, cost-effective, and high-performing - now and in the future.

Imagine constantly wondering if your app is leveraging the best Large Language Model (LLM) available. I’ve faced this challenge myself—struggling to keep up with the rapid pace of new models emerging, each promising better performance, lower costs, or faster speeds. Balancing quality, speed, and cost while choosing the right LLM can feel overwhelming. This Guided Project is designed to ease that pain, equipping you with the know-how and tools to master model selection using Promptfoo.

Every application—and even different features within the same app—has unique requirements. Some prioritize high-quality outputs, while others need results delivered quickly and affordably. Staying ahead means continually reassessing your choices to ensure optimal performance. This project will teach you how to navigate the ever-evolving landscape of LLMs, so you can confidently select the best models for your specific needs—now and in the future.

A Look at the Project Ahead

In this hands-on experience, we’ll start by introducing the fundamentals of the problem and how Promptfoo can help solve it. You’ll work with a simple demo app and delve into three real-world scenarios:

Adapting to New Models
When new models like Llama 3.2 are released with varying specs and costs, how do you decide if you should switch from your current model? We’ll explore how to evaluate new models based on your app’s specific requirements.
Handling Pricing Changes
What if a model you’re not currently using suddenly becomes more affordable? We’ll discuss how to assess whether switching models makes financial sense without sacrificing performance.
Regression Testing
Adding features or changing models can sometimes break existing functionality. You’ll learn how to perform regression testing to ensure your app continues to perform optimally, even as you make changes.

By the end of this project, you’ll be able to:

• Continuously evaluate and select the most suitable LLM models for different use cases within your app, balancing quality, speed, and cost.

• Adapt to changes in model availability and pricing, ensuring your app remains efficient and cost-effective.

• Implement regression testing to maintain and improve your application’s performance as you update models or add new features.

What You’ll Need

Before starting this project, you should have:

• Basic programming knowledge, preferably in Python, as we’ll work with code examples.

• Familiarity with LLMs and their applications—though we’ll cover the basics, prior understanding will help.

• A modern web browser like Chrome, Edge, Firefox, or Safari.

Why This Project Matters

In the fast-paced world of AI and LLMs, staying updated isn’t just advantageous—it’s essential. By learning how to effectively choose and switch between models, you ensure your app delivers the best possible performance to your users. This project doesn’t just teach you theory; it provides practical skills that you can apply immediately to your work.

Get Started

Embark on this journey to demystify LLM model selection. With the skills you’ll acquire, you’ll turn the daunting task of navigating the ever-changing LLM landscape into a strategic advantage. Keep your app ahead of the curve in quality, speed, and cost-effectiveness—now and for the future.

Offered By: IBMSkillsNetwork

Move to the best LLM model for your app

At a Glance

A Look at the Project Ahead

What You’ll Need

Why This Project Matters

Get Started