Earlier this year, I wrote Your AI product needs evals. Many of you asked, “How do I get started with LLM-as-a-judge?” This guide shares what I’

Creating a LLM-as-a-Judge That Drives Business Results

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2024-10-30 14:30:02

Earlier this year, I wrote Your AI product needs evals. Many of you asked, “How do I get started with LLM-as-a-judge?” This guide shares what I’ve learned after helping over 30 companies set up their evaluation systems.

Ever spend weeks building an AI system, only to realize you have no idea if it’s actually working? You’re not alone. I’ve noticed teams repeat the same mistakes when using LLMs to evaluate AI outputs:

The result? Teams end up buried under mountains of metrics or data they don’t trust and can’t use. Progress grinds to a halt. Everyone gets frustrated.

Tracking a bunch of scores on a 1-5 scale is often a sign of a bad eval process (I’ll discuss why later). In this post, I’ll show you how to avoid these pitfalls. The solution is to use a technique that I call “Critique Shadowing”. Here’s how to do it, step by step.

In most organizations there is usually one (maybe two) key individuals whose judgment is crucial for the success of your AI product. These are the people with deep domain expertise or represent your target users. Identifying and involving this Principal Domain Expert early in the process is critical.

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