  Large Language Models (LLMs) struggle with generating reliable outputs due to
outdated knowledge and hallucinations. Retrieval-Augmented Generation (RAG)
models address this by enhancing LLMs with external knowledge, but often fail
to personalize the retrieval process. This paper introduces PersonaRAG, a novel
framework incorporating user-centric agents to adapt retrieval and generation
based on real-time user data and interactions. Evaluated across various
question answering datasets, PersonaRAG demonstrates superiority over baseline
models, providing tailored answers to user needs. The results suggest promising
directions for user-adapted information retrieval systems.
