Retrieval-Augmented Generation (RAG) · TRACK 05/05
RAG Evaluation
Proving your pipeline works — and debugging it when it doesn't.
// THE TRACK
01 · START HEREMetrics That MatterYou'll know which metrics actually describe RAG quality and how to set up your first evaluation loop.BEGINNERThe Core MetricsYou will understand exactly what each of the four core RAGAS metrics measures, how the score is computed step by step, and which part of a broken RAG pipeline each metric points to.INTERMEDIATEFaithfulness vs RelevanceYou'll understand why answer quality needs two separate scores and how faithfulness and relevance are each measured.INTERMEDIATEPrecision, Recall & MRRYou'll be able to compute and interpret precision@K, recall@K, and MRR for your retriever, and know which one to optimize.INTERMEDIATERAGASYou'll understand the metrics RAGAS computes, how its LLM-as-judge scoring works, and how to interpret the numbers it gives you.INTERMEDIATEBuilding an Eval DatasetYou'll learn how to assemble a golden set of questions and answers that lets you measure RAG quality repeatably.INTERMEDIATELLM as a Judge for RAGYou'll learn how to use an LLM to grade RAG answers at scale and how to avoid the judge's known biases.INTERMEDIATEDebugging Bad RetrievalYou'll get a systematic checklist to find out why your RAG isn't retrieving the right chunks and how to fix each cause.INTERMEDIATE