LLM and RAG Evaluation: Metrics, Best Practices
This article provides a concise reference for evaluating large language models (LLMs) and retrieval-augmented generation (RAG) systems. It covers core metrics like accuracy, F1, BLEU, ROUGE, and perplexity, and highlights recent advancements in safety, alignment, semantic evaluation, hallucination detection, operational metrics, and multilingual performance. A practical bullet-point workflow is included for both research and production settings.