Using LLM-as-a-Judge For Evaluation: A Complete Guide
hamel.dev- Category
- Other
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- ARTICLE
- Builder
- @HamelHusain
- Added
- Jul 21, 2026
About
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. The Problem: AI Teams Are Drowning in Data 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: Too Many Metrics : Creating numerou
What it can do
Guide setup of an LLM-as-a-judge evaluation system
AI product and its outputs to evaluate → Step-by-step evaluation workflow
Apply the Critique Shadowing technique to align LLM judgments with expert judgment
Domain expert critiques and AI outputs → Calibrated LLM judge that mirrors expert evaluation
Identify the Principal Domain Expert whose judgment sets the evaluation standard
Organization's team and domain context → Selected key domain expert to guide evaluations
Diagnose common evaluation mistakes such as too many metrics or arbitrary scoring
Existing eval metrics and dashboards → Identified pitfalls and recommendations to fix them
Replace uncalibrated 1-5 scoring scales with validated pass/fail-style metrics
Subjective multi-dimensional scoring scheme → Clear, validated evaluation metrics
Validate that metrics reflect what matters to users and the business
Proposed evaluation metrics → Set of trusted, meaningful metrics
Why it made the leaderboard
A step-by-step methodology for building trustworthy LLM-as-a-judge systems, replacing arbitrary 1-5 scoring with 'Critique Shadowing' that anchors evals to a single domain expert's judgment. Useful if you're drowning in unvalidated eval metrics and can't tell whether your AI product actually works.
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