RAG Evaluation Metrics Explained | MRR, NDCG #RAG #AIAgents #GenerativeAI #LLM #finetuning

Description

No matter how advanced your RAG pipeline or AI agent system is, the real question during evaluation is: Did the system retrieve the right information? To answer this, engineers rely on several evaluation metrics that measure the quality of retrieval and ranking. In this video, we break down the most important RAG evaluation metrics with simple examples: MRR (Mean Reciprocal Rank) Measures how early the first correct answer appears in the ranking. Example: If the correct result appears at positions: 1st, 2nd, and 3rd across three queries. We calculate the reciprocal ranks and average them. Higher MRR means the correct answer appears earlier in the results. If you are building RAG systems, AI agents, or search systems, understanding these evaluation metrics is extremely important. Like the video and subscribe to SummarizedAI for more simple explanations on AI, RAG, and AI Agents. #RAG #AIAgents #MachineLearning #ArtificialIntelligence #GenerativeAI #LLM #VectorDatabase #AIEngineering #DataScience #AI #NDCG #Precision #Recall #MRR #TopKAccuracy