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Using LLM-as-a-Judge For Evaluation: A Complete Guide – Hamel's Blog - Hamel Husain

hamel.devSaved February 18, 202638 min

Technical Guide

Summary

A seven-step guide for building LLM-as-a-Judge evaluation systems, covering domain expert collaboration, dataset creation, pass/fail judgments with critiques, iterative prompt engineering, error analysis, and specialized judges.

Highlights

Seven-step framework for building LLM-as-a-Judge evaluators
Importance of domain experts and binary pass/fail judgments with critiques
Dataset creation using real and synthetic user interactions
Iterative prompt engineering until LLM judge converges with expert judgments
Error analysis, critique shadowing, and creating specialized judges

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Preview of Using LLM-as-a-Judge For Evaluation: A Complete Guide – Hamel's Blog - Hamel Husain

Hamel Husain · via Hamel Husain

Context

Audience

AI teams, machine learning engineers, applied researchers, and product managers building or improving evaluation systems for LLM-powered products

DomainMachine Learning

Formatlong-form article

Accessfree online

Topics

AI Model Assessment Machine Learning Operations Prompt Engineering LLM Evaluation Dataset Creation

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LLM evaluationprompt engineeringAI product evalssynthetic data generationfine-tuning LLMsML operations

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Using LLM-as-a-Judge For Evaluation: A Complete Guide – Hamel's Blog - Hamel Husain

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Using LLM-as-a-Judge For Evaluation: A Complete Guide – Hamel's Blog - Hamel Husain

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Using LLM-as-a-Judge For Evaluation: A Complete Guide – Hamel's Blog - Hamel Husain

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Using LLM-as-a-Judge For Evaluation: A Complete Guide – Hamel's Blog - Hamel Husain

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