Avoiding Pitfalls with LLM-as-a-Judge: A Guide to Effective AI Evaluation

Earlier this year, I wrote about the importance of evaluation in AI product development. Many readers asked how to get started with using Large Language Models (LLMs) as judges. This guide distills what I’ve learned from helping over 30 companies set up their evaluation systems.

The Problem: AI Teams Are Drowning in Data

Ever spent weeks building an AI system, only to realize you have no idea if it’s actually working? You’re not alone. I've seen teams repeatedly fall into these common traps when using LLMs to evaluate AI outputs:

• Too Many Metrics: Creating a plethora of measurements that become unmanageable.
• Arbitrary Scoring Systems: Using uncalibrated scales (like 1-5) across multiple dimensions, where the difference between scores is unclear and subjective. What makes something a 3 versus a 4? Different evaluators often interpret these scales differently.
• Ignoring Domain Experts: Not involving people who have deep subject matter expertise.
• Unvalidated Metrics: Using measurements that don’t truly reflect what matters to users or the business.

The result? Teams end up buried under mountains of metrics they can't trust or use. Progress grinds to a halt, and everyone gets frustrated. For example, it’s not uncommon for me to see dashboards that look like this:

An illustrative example of a bad eval dashboard

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

Step 1: Find the Principal Domain Expert

In most organizations, there is usually one (or 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.

Why Finding the Right Domain Expert Is So Important

• They Set the Standard: This person not only defines what is acceptable technically but also helps you understand if you’re building something users actually want.
• Capture Unspoken Expectations: By involving them, you uncover their preferences and expectations, which they might not be able to fully articulate upfront. Through the evaluation process, you help them clarify what a “passable” AI interaction looks like.
• Consistency in Judgment: People in your organization may have different opinions about the AI’s performance. Focusing on the principal expert ensures that evaluations are consistent and aligned with the most critical standards.
• Sense of Ownership: Involving the expert gives them a stake in the project, making them more invested in its success.

Step 2: Define Clear Evaluation Criteria

Once you have your Principal Domain Expert, work with them to define clear, actionable criteria. This involves:

• Identifying Key Metrics: Focus on metrics that directly impact user satisfaction and business goals.
• Calibrating Scoring Systems: Use well-defined scales with clear descriptions for each score. For example, a 3 might mean “meets basic requirements,” while a 4 means “exceeds expectations.”
• Creating Rubrics: Develop detailed rubrics that outline what constitutes a pass or fail in specific scenarios.

Example: Defining Evaluation Criteria

Let’s say you’re building an AI chatbot for customer service. Your Principal Domain Expert might help define criteria like:

• Accuracy of Responses: Does the chatbot provide correct information?
• User Friendliness: Is the interaction natural and easy to understand?
• Problem Resolution: Can the chatbot effectively resolve user issues?

Each criterion would have a well-defined scale and rubric.

Step 3: Implement Critique Shadowing

Critique Shadowing involves using an LLM to shadow the judgments of your Principal Domain Expert. This process helps you:

• Automate Initial Evaluations: Use the LLM to generate initial evaluations based on the criteria defined by the expert.
• Refine and Validate Metrics: Compare the LLM’s evaluations with those of the domain expert to refine and validate your metrics.
• Iterate and Improve: Continuously improve the