Empowering Large Language Model Reasoning : Hybridizing Layered Retrieval Augmented Generation and Knowledge Graph Synthesis

Retrieval Augmented Generation has improved LLM question answering significantly.However, this mechanism still produces hallucinations and structural incoherence in knowledge-intensive tasks.Additionally, many existing techniques neither holistically leverage multiple properties of text nor integrate diverse prompting and agenting frameworks.To address these limitations, this paper proposes a novel methodology that extracts and utilizes unstructured and structured properties of text to construct layered RAG pipelines designed to enhance complex LLM reasoning.Our approach synthesizes three distinct RAG methodologies, each specialized in various aspects: textual entity knowledge graph extraction (Textual Entity RAG); community summary and entity generation (Microsoft GraphRAG), and structural link navigation (MetaWiki RAG).By cumulatively layering these techniques along with advanced prompting and agentic evaluation, we aim to capture a more comprehensive context, enabling the model to generate well-structured responses that reflect all relevant attributes of the text.The proposed framework not only enhances existing RAG mechanisms but also demonstrates the effective integration of knowledge graphs.Additionally, it showcases the application of this framework to advanced answer generation using Wikipedia, with extensions to similar knowledge networks.This novel approach offers a robust solution for social recommender systems and other practical applications, delivering holistic outcomes by synthesizing diverse RAG techniques.