The shift toward systems that refine queries iteratively allows for better handling of complex, multi-document synthesis tasks.
Traditional RAG can struggle with highly structured, human-defined knowledge systems. eccentric_rag_2020_remaster
RAG allows models to leverage up-to-date, domain-specific, or private knowledge without retraining, making it highly suitable for fast-changing data environments. The shift toward systems that refine queries iteratively
It performs well in environments where labeled training data is scarce but large volumes of unstructured data are accessible. 3. Key Advancements and Trends or private knowledge without retraining
This report provides an overview of the landscape following its introduction in 2020, based on systematic literature reviews published through 2025. 1. Executive Summary: RAG Evolution (2020–2025)