Originally published at: https://developer.nvidia.com/blog/mastering-llm-techniques-evaluation/
Evaluating large language models (LLMs) and retrieval-augmented generation (RAG) systems is a complex and nuanced process, reflecting the sophisticated and multifaceted nature of these systems. Unlike traditional machine learning (ML) models, LLMs generate a wide range of diverse and often unpredictable outputs, making standard evaluation metrics insufficient. Key challenges include the absence of definitive ground…
The triangle in this article is a useful way to think about tradeoffs in LLM evaluation. One interesting extension in regulated environments is that evaluation signals eventually feed into governance structures such as the three lines of defense used in banking. Once LLM systems are integrated into operational workflows, evaluation metrics and monitoring signals often become inputs to ongoing performance monitoring for LLM and agentic AI in banking, where system behavior is continuously reviewed and escalated across engineering, validation, and audit functions.