Large Language Models (LLMs) are impressive, but they struggle to keep up with the ever-changing news cycle. Imagine asking an LLM about a recent event, only to receive outdated or generic information. This is a significant challenge for using LLMs in information retrieval, especially when dealing with rapidly developing situations. Researchers have developed a novel framework called "Neon" (News Entity-InteractiONs) to address this limitation. Neon works by extracting key entity interactions from news articles as they're published. This information forms a constantly updating knowledge graph where entities are connected by their actions and relationships, all time-stamped for precise retrieval. This dynamic knowledge graph acts as a real-time information source for LLMs. Instead of relying on static knowledge or generic web searches, Neon provides LLMs with up-to-the-minute information, dramatically improving their ability to answer questions about current events. The research demonstrates Neon's effectiveness using real-world queries from Bing search logs, focusing on a diverse range of entities like artists, companies, and political leaders. The results show that Neon substantially enhances the relevance, helpfulness, and accuracy of LLM responses. In essence, Neon transforms LLMs into powerful real-time question-answering systems. This breakthrough has implications for various applications, including news analysis, social media monitoring, and personalized information feeds. While promising, challenges remain. Researchers are exploring improvements to entity-interaction extraction and evaluation methods, paving the way for even more accurate and insightful real-time QA systems. Neon represents a significant step forward in bridging the gap between static LLM knowledge and the dynamic world of news, unlocking a new level of information access and understanding.