May 4
Loading video player...
📂 GitHub Repository: 🔗 https://github.com/yashjaincodex/rag-evaluation-ragas-youtube In this session, we’re learning how to evaluate RAG models using RAGAS — one of the most practical and important steps in building reliable AI applications. 🤖📊 If you’ve built a RAG pipeline but don’t know whether the answers are accurate, relevant, or grounded in the retrieved context, this video will help you understand how to measure RAG performance properly. By the end of this lesson, you’ll understand: ✅ What RAG evaluation means and why it is important ✅ What RAGAS is and how it helps evaluate RAG pipelines ✅ How to measure important metrics like faithfulness, answer relevancy, context precision, and context recall ✅ How to prepare evaluation datasets for RAG testing ✅ How to run RAGAS step by step in Python ✅ How to interpret RAGAS evaluation results ✅ How RAG evaluation helps improve AI application quality With hands-on examples, clear explanations, and a practical code walkthrough, you’ll gain the confidence to evaluate your RAG pipelines and build more accurate, reliable, and production-ready AI applications. 🚀 💬 Don’t forget to Like, Subscribe, and Comment what you learned today — your support keeps these AI coding lessons going strong! ✨ Before you sleep, make sure you’ve learned something new. ✨ #RAG #RAGAS #RAGEvaluation #LangChain #AI #LLM #ArtificialIntelligence #Python #AIEvaluation #RetrievalAugmentedGeneration #CodeBeforeYouSleep #YashJain