Large Language Models (LLMs) are impressive, but they can stumble when faced with multi-hop questions—those requiring multiple reasoning steps. Even worse, their knowledge can become outdated, leading to incorrect answers. Imagine asking an LLM, "On which continent will the next Olympic Games take place?" and getting a response about Tokyo when the games are actually in Paris. This problem is further compounded by the "cascading effect" where one outdated fact throws off the entire chain of reasoning in a multi-hop question. Researchers are tackling this issue through knowledge editing, but current methods struggle with conflicting information and inaccurate retrieval. A new approach called KEDKG (Knowledge Editing with Dynamic Knowledge Graphs) offers a promising solution. Instead of simply storing edited facts in memory, KEDKG builds a dynamic knowledge graph. This graph not only stores the updated information but also actively resolves conflicts. For example, if the location of the next Olympics changes again, KEDKG automatically updates the graph, preventing contradictions. Furthermore, KEDKG uses a fine-grained retrieval process to pinpoint the correct information within the knowledge graph, improving the accuracy of answers. Tests on challenging multi-hop question datasets show KEDKG outperforms existing methods, even rivaling the performance of powerful models like GPT-3.5-turbo-instruct on certain tasks. This innovative approach marks a significant step toward making LLMs more reliable and adaptable to our ever-changing world. While promising, challenges remain, including scaling the knowledge graph to handle a vast amount of information and ensuring the long-term consistency of edited knowledge. The ongoing research into dynamic knowledge graphs holds the potential to unlock even more robust and accurate reasoning capabilities in AI systems, bringing us closer to truly intelligent machines.