Phase 7: AI Evaluation & Observability · 55 min · Python · OpenAI SDK · LangSmith

LLM-as-a-Judge & AutoSxS Win-Rate Analytics

Use a frontier model to judge. But calibrate it first.

What you will learn

Implement LLM-as-a-Judge: using frontier models to score faithfulness, relevance, helpfulness
Calibrate judges: rubric design, few-shot examples, position bias mitigation, temperature settings
Run AutoSxS (Automatic Side-by-Side): comparing system versions, win-rate calculation
Apply statistical significance: sample sizes, confidence intervals, multiple comparison correction
Validate against human annotations: Cohen's kappa, disagreement analysis

Objective

What you'll learn

This lesson takes approximately 55 min. By the end, you will be able to:

Implement LLM-as-a-Judge: using frontier models to score faithfulness, relevance, helpfulness
Calibrate judges: rubric design, few-shot examples, position bias mitigation, temperature settings
Run AutoSxS (Automatic Side-by-Side): comparing system versions, win-rate calculation
Apply statistical significance: sample sizes, confidence intervals, multiple comparison correction
Validate against human annotations: Cohen's kappa, disagreement analysis

The Problem

Using an LLM to evaluate another LLM's output — "LLM-as-a-Judge" — is the most scalable evaluation method available. But it's not reliable out of the box: judges have biases, position preferences, and length preferences that skew scores. This lesson covers implementation, calibration techniques, AutoSxS win-rate evaluation, and the prompts that produce reliable judge scores.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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