In this video, Noah, a software engineer at NE 4 and an LLM enthusiast, shares his insights on converting unstructured text data into knowledge graphs using large language models (LLMs). He begins by highlighting the challenges of working with unstructured text data, which is difficult to process meaningfully. Noah then introduces the concept of knowledge graphs as a structured format to represent information extracted from text. He explains that LLMs, such as ChatGPT and Bard, are generative AI capable of processing text and generating summaries or key points. Noah discusses the pipeline developed in a previous project, which consists of chunking text, extracting nodes and relationships, and entity disambiguation. He demonstrates this process using examples and shows a demo of the pipeline applied to the James Bond Wikipedia page, resulting in a comprehensive knowledge graph. Noah acknowledges the limitations and challenges, such as accuracy issues, data bias, and model limitations, but remains optimistic about the potential improvements with further research and development.