Large Language Models (LLMs) have revolutionized how we interact with technology, demonstrating remarkable abilities in general content creation. However, their performance in specialized areas, like medicine, has been hampered by a lack of specific knowledge. Think of it like asking a brilliant generalist to perform brain surgery—they have the general intelligence, but not the specialized medical training. Traditional methods of incorporating knowledge, such as continual pre-training on domain-specific data, can be computationally expensive and time-consuming. This research introduces a clever new approach: infusing LLMs with structured knowledge from Knowledge Graphs (KGs). KGs organize information into interconnected entities and relationships, offering a more efficient way to inject domain expertise. However, simply plugging a KG into an LLM isn’t enough. Two main roadblocks exist: knowledge mismatch (public KGs often lack specific domain details) and poor information compliance (LLMs struggle to process the structured format of KGs). This research tackles these challenges head-on. First, it proposes a method to efficiently build a domain-specific KG using the LLM itself. By training the LLM on a small set of labeled examples, it can extract relevant knowledge from a large domain-specific corpus and construct a custom-fit KG. This process is like giving our generalist surgeon a crash course in anatomy and surgical techniques. Second, a novel three-stage process aligns the LLM with the newly built KG. The initial “pre-learning” phase teaches the LLM the language of the KG. Next, supervised fine-tuning refines the LLM's ability to answer specific questions using the KG. Finally, a unique feedback loop uses the KG to assess the accuracy of the LLM’s responses, further honing its knowledge integration. Tests on medical question-answering datasets show significant improvements over existing methods. The results point towards a future where LLMs can seamlessly integrate external knowledge, opening doors to more reliable, accurate, and insightful AI applications. This research is a crucial step in bridging the gap between general AI brilliance and domain-specific expertise. Imagine a world where AI can provide expert-level advice in any field, from legal consultations to scientific research—this new approach brings us closer to that exciting reality. While promising, some limitations remain. The method's success heavily depends on the quality of the KG. Furthermore, evaluating knowledge relevance within the KG is complex, and future research will need to address this challenge. This innovative research offers a powerful new framework for enhancing LLMs with knowledge graphs, paving the way for a new era of AI potential.