How to Measure RAG Performance | Recall, Precision, MRR & NDCG

Description

In this video, we will understand the most important RAG evaluation metrics used in AI systems, Retrieval-Augmented Generation (RAG) pipelines, and AI agents. No matter how advanced your RAG system is, one important question always remains: Did the system retrieve the correct information? To answer this, we explore some of the most widely used retrieval evaluation metrics with simple examples: 1. Top-K Accuracy / Hit Rate 2. Recall@K 3. Precision@K 4. Mean Reciprocal Rank (MRR) We will learn: 1. What each metric means 2. Why these metrics matter in RAG systems 3. How the formulas work 4. Practical examples for each metric 5. How ranking quality impacts AI responses This video is perfect for: 1. AI Engineers 2. LangChain Developers 3. LangGraph Developers 4. RAG Developers 5. Machine Learning Engineers 6. Anyone building AI search or retrieval systems If you're building chatbots, AI agents, semantic search systems, or enterprise RAG applications, understanding these metrics is extremely important. #RAG #AI #LLM #GenerativeAI #LangChain #LangGraph #MachineLearning #AIEngineering #VectorDatabase #RetrievalAugmentedGeneration #NDCG #MRR #PrecisionAtK #RecallAtK #TopKAccuracy