Traditional RAG can struggle with highly structured, human-defined knowledge systems.
The shift toward systems that refine queries iteratively allows for better handling of complex, multi-document synthesis tasks. eccentric_rag_2020_remaster
To reduce hallucination rates and overcome the limitations of static, outdated knowledge within parametric-only models. diversifying into hybrid retrievers
It eliminates the need for expensive, frequent model fine-tuning. iterative retrieval loops
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)
The field has moved beyond basic RAG, diversifying into hybrid retrievers, iterative retrieval loops, and graph-based retrieval systems.