Knowledge graphs, representing the world's information as interconnected entities, are invaluable for AI. But they often have missing links, limiting their power. Imagine trying to navigate a map with missing roads—frustrating, right? Traditional methods struggle to fill these knowledge gaps due to the sheer complexity and scale of information. However, Large Language Models (LLMs) like GPT-3 offer a solution by leveraging their vast knowledge stores. Fine-tuning these LLMs works but comes at a computational cost—think of the time and resources needed to re-educate an expert on a slightly new task. So how can we tap into the power of LLMs effectively without excessive overhead? This research introduces an ingenious method: using "frozen" LLMs. Instead of retraining the entire model, they inject carefully crafted prompts and detailed entity descriptions into the LLM's existing structure. It's like giving precise instructions to a knowledgeable expert, focusing their attention on the specific missing information needed. This approach bypasses extensive retraining, saving vast computational resources and accelerating the process of knowledge graph completion. The results are impressive: comparable accuracy to fully fine-tuned models, but with a remarkable 188x boost in memory efficiency and a 13x speedup. This research unlocks a new level of efficiency in using LLMs for KGC, paving the way for building more complete and powerful knowledge graphs. While this method currently doesn't work with all types of LLMs, it offers a major leap toward using frozen LLMs for knowledge graph completion and suggests exciting future directions for more efficient and scalable AI knowledge management.