Phase 3: Enterprise RAG Systems · 55 min · Python · RAGAS · LangSmith

RAG Evaluation Harness

If you can't measure retrieval quality, you're flying blind.

What you will learn

Implement RAGAS metrics: faithfulness, answer relevancy, context precision, context recall
Construct test datasets: 50-200 (query, ideal-answer, source-document) triples from real traffic
Set up automated grading: LLM-as-a-judge for faithfulness/relevancy, human review for critical subset
Hold out a test set to detect overfitting during retrieval tuning
Run the eval harness in CI on every prompt/model/chunking change

Objective

What you'll learn

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

Implement RAGAS metrics: faithfulness, answer relevancy, context precision, context recall
Construct test datasets: 50-200 (query, ideal-answer, source-document) triples from real traffic
Set up automated grading: LLM-as-a-judge for faithfulness/relevancy, human review for critical subset
Hold out a test set to detect overfitting during retrieval tuning
Run the eval harness in CI on every prompt/model/chunking change

The Problem

If you can't measure retrieval quality, you're flying blind. A RAG evaluation harness gives you quantitative metrics for every component: retrieval precision/recall, context relevance, faithfulness, answer correctness. Without these metrics, you can't tell if a change actually improved the system.

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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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