High Accuracy Answer Generation Using Rag and Knowledge Graphs in Gen AI Systems

Abstract

The rapid growth of Generative Artificial Intelligence (GenAI) has transformed information retrieval and automated reasoning across various domains. Large Language Models (LLMs) such as GPT, LLaMA, and PaLM have demonstrated strong natural language generation capabilities but continue to struggle with hallucination, factual inconsistency, and lack of grounded reasoning. These issues reduce reliability, especially in knowledge-intensive applications. In response, Retrieval-Augmented Generation (RAG) has emerged as an effective solution by enabling LLMs to retrieve contextual documents dynamically. However, retrieval alone does not ensure semantic understanding or multi-hop reasoning. Knowledge Graphs (KGs) introduce structured, interconnected, and explainable representations of knowledge that enhance contextual precision. This study presents a comprehensive review and design of a GenAI system that integrates RAG with Knowledge Graphs for high-accuracy answer generation. We analyze the limitations of standalone LLMs, discuss the role of RAG in information grounding, and highlight how KGs support deeper reasoning and relationship modelling. The objective is to demonstrate that combining retrieval-based grounding with graph-based reasoning significantly improves the accuracy, factuality, and interpretability of GenAI systems. Experimental analysis and existing literature indicate that the hybrid RAG + KG framework achieves superior performance in accuracy and reduces hallucination compared to conventional LLMs.