RAG Evaluation with RAGAS Explained | RAG for ML #09

Description

You have been improving your RAG system for weeks. But how do you actually know it is getting better? Without measurement you are flying blind. RAGAS gives you four concrete metrics that tell you exactly where your system is failing. In this episode we cover: Why you need automated evaluation for RAG The four core RAGAS metrics explained Faithfulness — is the answer grounded in the context? Answer relevancy — does the answer address the question? Context precision — are the right chunks ranked highest? Context recall — are all relevant chunks being retrieved? Running a full RAGAS evaluation in Python Next up: Hallucination Detection