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Free Lesson

How to Trust Your LLM Judge: A Practical Framework

Hosted by Panos Alexopoulos

In this video

What you'll learn

Understand what makes LLM judges unreliable

Understand what makes LLM judges unreliable and recognize how biases and design choices can affect their judgments.

Evaluate your LLM judge systematically

Apply practical techniques to measure agreement, detect bias, and assess the stability of your evaluator.

Iteratively improve your LLM judges

Use evaluation results to diagnose weaknesses and systematically refine your judges for greater reliability.

Why this topic matters

As LLM judges become a common part of AI evaluation pipelines, their reliability becomes increasingly important. Treating their outputs as objective scores without validating them can lead to misleading conclusions, as bias, instability, and seemingly minor design choices can significantly affect their judgments. This Lightning Lesson introduces a practical framework for evaluating your LLM judge and determining when its results can be trusted.

You'll learn from

Panos Alexopoulos

Data & AI Architect | Author | Educator

Panos Alexopoulos is a Data and AI practitioner, author, and educator with more than 20 years of industry experience designing and delivering data and AI solutions across multiple domains.

He is the author of Semantic Modeling for Data (O’Reilly Media, 2020) and is currently writing Evaluating AI Systems, a practitioner-focused book on systematic AI evaluation, to be published by Manning in 2027.

Panos has designed and delivered more than 30 courses and workshops on Data and AI topics, reaching thousands of practitioners worldwide. His teaching portfolio includes two highly popular O’Reilly live courses on AI Evaluation (4 editions so far) and GraphRAG (11 editions so far), as well as an MSc-level course on Knowledge Graphs and LLMs at the Athens University of Economics and Business.

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Building Trustworthy LLM Judges
Panos Alexopoulos
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