Imagine having an AI assistant that truly understands your unique needs and preferences. This isn't science fiction, but the promise of personalized large language models (LLMs). Current LLMs, while impressive, often struggle to adapt to individual users' specific knowledge. Existing personalization methods are computationally expensive and can have unpredictable effects on the model's overall performance. However, a new technique called Knowledge Graph Tuning (KGT) offers a more efficient and transparent approach. Instead of directly modifying the LLM's internal parameters, KGT creates a personalized knowledge graph for each user. This graph stores facts and relationships specific to the user, like dietary preferences of their pet or details about their work. When the user interacts with the LLM, it retrieves relevant information from this personalized graph, allowing it to provide tailored responses. This method is not only faster but also more interpretable. Researchers tested KGT with leading LLMs like GPT-2, Llama 2, and Llama 3, and found significant improvements in personalization effectiveness while reducing computational costs. The ability to easily add or remove knowledge from the graph makes it ideal for real-time personalization, constantly adapting to the user's feedback. While KGT relies on the LLM's ability to understand instructions, the increasing sophistication of these models suggests this won't be a limiting factor for long. KGT represents a significant step towards truly personalized AI, paving the way for more intuitive and user-centric applications.