A Survey of Personalization: From RAG to Agent

We cover the comprehensive survey on the integration of personalization within Large Language Models (LLMs), specifically focusing on the evolution from Retrieval-Augmented Generation (RAG) frameworks to agent-based architectures. It systematically examines how personalization is incorporated across the pre-retrieval, retrieval, and generation stages of RAG, and extends this analysis to the more advanced functionalities of Personalized LLM-based Agents, including user understanding, planning and execution, and dynamic content generation. The survey also highlights key datasets, evaluation metrics, challenges, and future research directions in this rapidly evolving field, providing a valuable resource for researchers.