How to Evaluate Your RAG Pipeline with RAGAS — Full End-to-End Project

Description

Stop guessing if your RAG pipeline actually works. In this end-to-end project, I'll show you exactly how to evaluate any production RAG system using RAGAS — the open-source framework every AI Architect should know. If you're a senior developer building RAG pipelines for production (healthcare, finance, insurance, enterprise search), you've probably hit this wall: "How do I PROVE my RAG is good enough to ship?" RAGAS solves this. By the end of this video, you'll have a complete evaluation system you can apply to your own RAG project today. 🎯 WHAT YOU'LL LEARN: ✅ What RAGAS is and why it's the gold standard for RAG evaluation ✅ The 4 core metrics: Faithfulness, Answer Relevance, Context Precision, Context Recall ✅ How to set up RAGAS in Python from scratch ✅ Building a synthetic test dataset for evaluation ✅ Running end-to-end evaluation on a real RAG pipeline ✅ Interpreting the metrics and fixing failure modes ✅ Production patterns: when to evaluate, how often, and what to track 🛠️ TECH STACK USED: - Python 3.10+ - RAGAS - LangChain / LlamaIndex - OpenAI / Claude (for LLM-as-judge) - Vector DB (Chroma / Qdrant) 📂 CODE & RESOURCES: Download source code: https://drive.google.com/file/d/1VK-m-m45fPYGe0P397P-80oYnTI3eiRy/view?usp=sharing 🚀 WANT TO BECOME AN AI ARCHITECT? I run GenAI Elite — a live mentorship program for senior devs transitioning to AI Architect roles. We cover production RAG, Agents, MCP, and real enterprise architecture (not toy projects). → Apply here: https://connect.genaielite.com/live-workshop 🔔 SUBSCRIBE for weekly videos on production GenAI, RAG, LangGraph, MCP, and the path from Senior Dev to AI Architect. 💬 QUESTIONS? Drop them in the comments — I read and reply to every one in the first 48 hours. #RAGAS #RAGEvaluation #LLM #GenAI #AIArchitect #RAGTutorial #LangChain #LlamaIndex #ProductionAI #AIEngineering #SeniorDeveloper #RAGPipeline #VectorDatabase #PythonAI #MachineLearning