Phase 3: Enterprise RAG Systems · 55 min · Python · RAGAS · LangSmith
RAG Evaluation Harness
If you can't measure retrieval quality, you're flying blind.
Hiring signal: Take-home projects at OpenAI require an evaluation harness with at least 3 metrics. Candidates who implement RAGAS metrics (faithfulness, answer relevancy, context precision/recall) with 50-200 test triples pass. Candidates who submit RAG systems without evaluation fail. The eval harness is the 2026 non-negotiable that separates professional FDEs from hobbyists.
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
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.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers RAGAS Metrics: The Four Core Metrics, Constructing Test Datasets, Automated Grading: LLM-as-a-Judge, Running the Eval Harness in CI, Practical Application, What Hiring Managers Look For, Resources, Key Takeaways, Next Steps — plus a hands-on lab, quiz, and project artifact.
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