RAG Evaluation Metrics Explained: Context Precision, Recall, Relevancy & Faithfulness скачать в хорошем качестве

RAG Evaluation Metrics Explained: Context Precision, Recall, Relevancy & Faithfulness

In this video, I explain the key evaluation metrics used in RAG (Retrieval-Augmented Generation) systems. Learn what Context Precision, Context Recall, Response Relevancy, and Faithfulness mean in simple terms with examples. These metrics help us understand how well a RAG model retrieves and generates accurate responses. Whether you're a beginner or working on a GenAI project, this will help you understand how to evaluate your RAG system effectively. 📌 Topics Covered: What is RAG? Context Precision vs Context Recall Response Relevancy meaning Faithfulness in RAG outputs Why these metrics matter 0:00 Introduction 0:30 What is RAG? 1:30 What is Embeddings? 2:00 What is Vector Database? 2:42 Evaluation usecase Introduction 4:56 RAG Metrics Mindmap 6:48 Precision & Recall 9:45 Context Precision 13:43 Context Recall 15:34 Response Relevancy 17:38 Faithfulness 19:28 Conclusion 📢 Don't forget to LIKE, SUBSCRIBE, and COMMENT your thoughts below! 🔔 Subscribe for more AI updates! Web - https://shrijayan.cpluz.com X - https://x.com/rshrijayan Linkedin -   / shrijayan