Deanna Emery

AI search is becoming the front door to information, whether through Retrieval-Augmented Generation (RAG), Search-Augmented Generation (SAG), or custom agents that synthesize answers on top of indexed content. As users rely more heavily on these systems, evaluating their quality becomes mission-critical. But traditional metrics like precision and recall don’t capture the full picture.

Retrieval Augmented Generation (RAG) has become a cornerstone for integrating domain-specific content and addressing hallucinations in AI applications. As the adoption of RAG solutions intensifies across industries, a pressing challenge emerges: understanding and identifying where within the complex RAG framework changes and improvements can be made.