Retrieval-Augmented Generation (RAG)— From Modular to Agentic Systems

Abstract

Retrieval-Augmented Generation (RAG) has emerged as the dominant strategy to ground LLM outputs in retrieved evidence to reduce hallucinations and increase factuality, yet RAG itself introduces new challenges: noisy retrieval, knowledge conflicts, and wasted computation from unnecessary retrieval. This tutorial provides an in-depth treatment of modern RAG based on AI-facilitated systematic analysis of ~2000 recent papers (2020--2026). We trace the RAG pipeline from its modular foundations through graph-enhanced reasoning to the latest RL-driven agentic architectures, covering each stage---triggering, query rewriting, retrieval, post-processing, generation, and knowledge-graph integration---with running examples, method comparisons, and benchmark results. Attendees will leave with a structured roadmap, evidence-backed practical recommendations, and a clear map of open research opportunities.