rag evaluation

The article outlines how to evaluate Retrieval‑Augmented Generation systems across the stack: retriever (recall@k, MRR, nDCG), context quality (precision, coverage), and generator outputs (answer relevance, faithfulness/groundedness, hallucinations). It covers building test sets, LLM‑as‑judge vs human labels, offline/online testing, monitoring, and practical tactics to debug and improve RAG.