Imagine sifting through mountains of technical documents, searching for that one crucial piece of information. It's a tedious, time-consuming process, and prone to errors. But what if AI could do the heavy lifting? Researchers are exploring how Large Language Models (LLMs) can automatically extract knowledge and build structured knowledge graphs, revolutionizing how we access and understand complex information. One innovative approach, called OntoKGen, uses LLMs and a clever Chain of Thought (CoT) prompting technique to build these knowledge graphs. Think of it like giving the LLM a roadmap, guiding it step-by-step to extract key concepts, relationships, and properties from text. This interactive system allows users to refine the process, ensuring the knowledge graph aligns with their specific needs. It's like having a personalized AI research assistant that can summarize and organize complex information, making it easier to visualize, analyze, and draw meaningful conclusions. The research team tested OntoKGen on a dense technical document related to semiconductor manufacturing equipment. The results? A detailed knowledge graph that accurately captured the information, demonstrating the potential of LLMs to transform how we handle complex technical documentation. This automated approach not only saves time and reduces errors, but also opens doors to more sophisticated analysis and decision-making. Imagine querying the knowledge graph to uncover hidden relationships or using it to power smarter AI systems. This is just the beginning, and future research promises even more exciting developments, like real-time manipulation of data and integration with other advanced AI technologies. The potential of LLMs to unlock knowledge is vast, and we're only just scratching the surface.