RAG Evaluation Explained: Precision, Recall, Faithfulness, Relevance, BLEU, ROUGE, F1 and RAGAS

Description

Evaluating RAG systems requires more than checking how similar an answer is to a reference. Traditional metrics like BLEU, ROUGE, and F1 measure wording, coverage, and factual correctness, but RAG introduces a new layer through retrieval and grounding. In this video, we break down the full evaluation pipeline for RAG systems. We explore context precision, context recall, answer relevance, faithfulness, groundedness, and how LLM-as-a-Judge fits into modern evaluation. Finally, we look at RAGAS, the open-source framework that standardizes these evaluations using embeddings and LLM-based scoring. This video is designed for engineers, researchers, and anyone building retrieval-augmented generation systems who wants a clear and structured understanding of how to measure quality end-to-end. Chapters 0:00 – Introduction 00:32 – BLEU Explained with Examples 2:09 – ROUGE Explained with Examples 3:36 – F1 Score Explained with Examples 5:10 – RAG Needs New Metrics 6:07 – Context Precision with Examples 6:53 – Context Recall with Examples 7:34 – Answer Relevance with Examples 8:07 – Faithfulness with Examples 8:40 – Groundedness with Examples 9:20 – LLM-as-a-Judge 10:17 – What RAGAS Is and Why It Matters 11:25 – Recap #RAG #RAGAS #LLM #RetrievalAugmentedGeneration #AIEngineering