Imagine giving an AI a stack of textbooks and it not only reads them but also creates its own perfectly organized notes. That's the idea behind new research that empowers Large Language Models (LLMs) to build their own knowledge retrieval index. Retrieval-Augmented Generation (RAG) is a powerful technique to give LLMs access to real-time information, but building and maintaining these knowledge stores is a huge undertaking. This new method, called Pseudo-Graph Retrieval-Augmented Generation (PG-RAG), treats the LLM like a student. It's given raw information and tasked with summarizing it into concise, interconnected notes, forming a 'pseudo-graph' database. When the LLM needs to answer a question, it uses this pseudo-graph like a student flipping through notes, finding the most relevant information pathways. The research shows PG-RAG significantly outperforms existing methods, especially in complex, multi-document tasks. It's a big step towards making LLMs more efficient and accurate by allowing them to structure and access knowledge more like humans do. This self-learning approach could revolutionize how we build and use knowledge bases for AI, but challenges remain, such as handling extremely long texts and the computational cost of using LLMs for initial knowledge extraction. Future research will likely focus on refining the 'walking' algorithms that explore the pseudo-graph and developing more efficient methods for knowledge compression and integration.