Eval Engineering for AI Developers - Lesson 2: Observability in AI apps
Learn Eval Engineering in this free, 5-part, hands-on course.
90% of AI agents don't make it successfully to production. The biggest reason is the AI engineers building these apps don't have a clear way of evaluating that these agents are doing what they should do, and using the results of this evaluation to fix them.
In this course, you will learn all about evals for AI applications. You'll start with some out-of-the-box metrics and learn about evals, then move onto understanding observability for AI apps, analyzing failure states, defining custom metrics, then finally using these across your whole SDLC.
This will be hands on, so be prepared to write some code, create some metrics, and do some homework!
In this second lesson, you will
Use observability to visualize the components of a typical multi-agent AI application
Learn about the different components that make up these applications
Apply some out-of-the-box metrics to start to get an understanding of how your application is working
Prerequisites:
A basic knowledge of Python
Access to an OpenAI API key
A free Galileo account (we will be using Galileo as the evals platform)
Catch the recording of the previous lesson
Lesson 1: Watch on YouTube
Future lessons
Lesson 3: https://luma.com/3k99shl1
Lesson 4: https://luma.com/x2ztpa4f
Lesson 5: https://luma.com/esoi6izo