Integrating Graph Neural Networks (GNNs) with Large Language Models (LLMs) enhances the accuracy of relational queries by providing structured graph context, which is particularly beneficial in sectors like finance, healthcare, and social media for tasks such as fraud detection, personalized treatment recommendations, and user interaction analysis. This hybrid approach, supported by frameworks like PyTorch Geometric and Deep Graph Library, reduces query latency by up to 70%, making it suitable for real-time analytics and recommendation systems, as demonstrated by companies like Pinterest and Alibaba. Despite the advantages, challenges such as complexity, bias in graph data, and scalability issues remain, necessitating careful pipeline management. Tools like LangChain and FalkorDB facilitate the orchestration of these systems, ensuring more accurate and efficient handling of structured queries and minimizing the risk of hallucinations in LLMs, as highlighted by industry experts.