The recent paper on Zero-RAG introduces a novel approach to Retrieval-Augmented Generation (RAG) designed to reduce knowledge redundancy in large language models (LLMs). This method specifically targets the hallucination issues that frequently affect LLMs by leveraging their expanded internal knowledge more effectively. By minimizing redundant information retrieval, Zero-RAG aims to enhance both the performance and efficiency of these models. The approach builds on the premise that reducing unnecessary external knowledge retrieval can lead to more accurate and reliable outputs. As a result, Zero-RAG holds potential for improving the overall utility of LLMs in various applications. This development aligns with ongoing efforts in the AI research community to address common limitations of large-scale language models. The paper’s findings contribute to a growing body of work focused on optimizing knowledge integration within LLMs to achieve better generation quality.