At a Glance

Name: Build an Eval Harness for Your AI App with LLM-as-a-Judge
Price: Free CAD
Rating: 0.0 (0 reviews)
Author: aryan_kansagara_2

Build a Python eval harness that uses an LLM to grade your AI app’s answers, then check whether those grades can be trusted. Using IBM watsonx.ai and a sample support chatbot, you’ll create a golden set, write a judge rubric, turn messy model replies into clean scores, and test the judge against human scores with Cohen’s kappa. You’ll also catch a judge that rewards longer answers, and fix it. No stats or machine learning background needed, just basic Python.

You built an AI app. Maybe it’s a chatbot, a summarizer, or a support assistant. You tried it on a few examples and it looked great. Then you changed the prompt and tried those same few examples again. Did it get better or worse? Most people can’t say, because wrong answers from an AI sound just as confident as right ones.

In this guided project, you’ll build an eval harness: a small Python program that uses one AI model to grade another AI’s answers. This approach is called LLM-as-a-judge. You’ll test it on a support chatbot for a fictional cloud storage company. Along the way you’ll find out that a judge’s scores mean nothing until you check them, and that most judge problems are fixed by writing better instructions, not by switching to a bigger model.

A Look at the Project Ahead

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

Build a golden set of example answers with trusted human scores to test against
Write a judge prompt (rubric) that has an LLM score answers against the source facts
Turn messy model replies into clean, reliable scores your code can use
Check the judge against human scores using agreement and Cohen’s kappa, and spot when a high score is meaningless
Detect length bias (judges rewarding longer answers) and remove it by improving the rubric

You’ll leave with a working eval harness you can adapt to test your own AI apps.

What You’ll Need

Comfort reading and running basic Python. No background in statistics or machine learning is needed, because every concept is explained as you go.
Nothing to install or set up. The IBM Skills Network Labs environment comes with everything ready, including access to IBM watsonx.ai, so you don’t need your own API key.
About 45–60 minutes.
A current version of Chrome, Edge, Firefox or Safari.

Offered By: IBMSkillsNetwork

Build an Eval Harness for Your AI App with LLM-as-a-Judge

At a Glance

A Look at the Project Ahead