RAG Evaluation and Grounding

Retrieval-Augmented Generation (RAG) systems combine document retrieval with language generation to ground responses in external knowledge. However, blindly chaining retrieval and generation without rigorous evaluation leads to hallucinations, off-topic answers, and unverifiable claims. This series teaches you how to systematically measure RAG quality through retrieval metrics (precision, recall, nDCG), generation metrics (faithfulness, relevance), and grounding techniques (citation enforcement, context attribution). You'll learn to build golden evaluation datasets, implement RAGAS-style automated scoring, detect when models fabricate information, run regression tests against baselines, and close the feedback loop to continuously improve your RAG pipeline.

By the end of this series, you will be able to instrument a production RAG system with comprehensive evaluations, quantify hallucination risk, and iteratively refine retrieval and generation parameters based on real metrics instead of gut feel.

Articles in this series

• RAG Evaluation Metrics: Step-by-Step Guide
• Retrieval Metrics Explained: Precision and Recall
• Building Golden Datasets for RAG Systems
• What Is Faithfulness Scoring in RAG?
• Context Relevance Metrics: Measure Retrieval Quality
• RAG Hallucination Detection: How to Identify False Content
• Enforcing Citations in RAG: Citation Grounding
• RAGAS Framework: Automated RAG Evaluation
• Running Regression Tests for RAG Systems
• RAG Evaluation Loop: Continuous Improvement