About This Tutorial

Retrieval-Augmented Generation (RAG) has become a foundational paradigm for enhancing large language models (LLMs) with external knowledge, playing an important role in modern information retrieval and knowledge-intensive tasks. Standard RAG systems typically adopt a static retrieve-then-generate pipeline and rely on in-context knowledge injection, which can be suboptimal for complex tasks that require multihop reasoning, adaptive information access, and deeper integration of external knowledge. Motivated by these limitations, the research community has moved beyond static retrieval and in-context knowledge injection. 

Among the emerging directions, this tutorial delves into two rapidly growing and complementary research directions on RAG: Dynamic RAG and Parametric RAG. Dynamic RAG explores when and what to retrieve during the LLM's generation process, enabling real-time adaptation to its evolving information needs. Parametric RAG rethinks how the retrieved knowledge should be incorporated, moving from input-level to parameter-level knowledge injection for improved efficiency and effectiveness. This tutorial offers a comprehensive overview of recent advances in both directions. It also shares theoretical foundations and practical insights to support and inspire further research in RAG.