Modern AI Engineering

Lesson 14.2 · 23 min · video lesson

LLM-as-Judge: Automating Evaluation with Another Model

If we need to grade 10,000 chatbot answers by tomorrow, could another LLM do the grading, and could we trust it?

In short: LLM as a judge means using a capable language model, given a clear rubric, to evaluate the outputs of another model or system. It scales open-ended evaluation that string metrics cannot handle and that humans cannot afford to do at volume. Judges come in pointwise, pairwise and reference-guided forms; they work best with explicit criteria and step-by-step reasoning, and they must be checked for biases such as position and length preference and validated against human labels.

What you will learn

  • What is LLM as a Judge?
  • Why do we need LLM as a Judge?
  • How does LLM as a Judge work?
  • Types of LLM as a Judge.
  • Steps to build an LLM Judge.
  • A prompt template for LLM as a Judge.
  • Chain-of-thought judging (G-Eval).
  • Biases in LLM as a Judge.
  • Best practices for LLM as a Judge.
  • Real-world use cases of LLM as a Judge.

In this lesson

  1. What is LLM as a judge, and why do we need it?
  2. How does LLM as a judge work?
  3. Types of LLM as a judge
  4. Steps to build an LLM judge
  5. A prompt template for LLM as a judge
  6. Chain-of-thought judging (G-Eval)
  7. Biases in LLM as a judge
  8. Best practices and real-world use cases
  9. Worked example, step by step
  10. Practice: try it yourself

Key terms in this lesson

LLM as a judge
Using a language model with a rubric to evaluate the outputs of an LLM system.
Rubric
The written criteria and score definitions the judge applies.
Pairwise comparison
A judging mode where the judge picks the better of two outputs for the same input.
G-Eval
A judging recipe using generated evaluation steps and probability-weighted scores.
Position bias
A judge's tendency to prefer an answer because of where it appears, not its quality.
Verbosity bias
A judge's tendency to rate longer answers higher regardless of quality.
Cohen's kappa
An agreement score between two raters that corrects for agreement expected by chance.

This lesson is part of the Generative AI Engineering and Complete AI Engineer tracks. See plans and pricing to open the full lesson, its video, labs and quiz, or try the free lesson first (free account needed).

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