Explore the full engineering blog here: https://developer.nvidia.com/blog/mastering-llm-techniques-evaluation/

This NVIDIA technical blog post discusses the challenges and strategies for evaluating large language models (LLMs) and retrieval-augmented generation (RAG) systems. It highlights the inadequacy of traditional metrics due to LLMs' diverse and unpredictable outputs, emphasizing the need for robust evaluation techniques. The post introduces NVIDIA NeMo Evaluator, a tool designed to address these challenges by offering customizable evaluation pipelines and various metrics, including both numeric and non-numeric approaches like LLM-as-a-judge. Several academic benchmarks and evaluation strategies are detailed, along with specific metrics for assessing RAG systems' retrieval and generation components. The authors ultimately promote NeMo Evaluator as a solution to streamline the complex process of LLM evaluation.