How I Evaluate RAG Systems — RAGAS Full Walkthrough

Most RAG pipelines look fine until they go to production. In this video, I walk you through exactly how I evaluate RAG systems using RAGAS — the 4 metrics that matter and what your scores are actually telling you. If you're building RAG pipelines and relying on "it feels accurate" as your quality check — this video is for you. In this full walkthrough, I break down how I evaluate production RAG systems using RAGAS (Retrieval Augmented Generation Assessment) — the same approach I use when building enterprise AI systems. You'll learn: What RAGAS actually measures and why most engineers skip this step The 4 core metrics: Context Precision, Context Recall, Faithfulness, and Answer Relevancy What good scores look like vs. what needs improvement How to interpret your results and which metric to fix first Common RAG mistakes that tank your evaluation scores — and how to fix them Whether you're building a document Q&A system, an enterprise chatbot, or a multi-agent RAG pipeline — you need to know if your system is actually working before it hits production. This isn't a toy example. This is how evaluation works in real enterprise projects. Who this is for: Senior engineers transitioning into AI/ML, backend developers building RAG systems, and anyone who wants production-grade GenAI knowledge — not just tutorials.