Retrieval-Augmented Generation (RAG) has become the go-to strategy for grounding large language models with contextual data in AI-driven applications. Yet, despite its promise, RAG often struggles with accuracy, retrieving irrelevant or suboptimal results that dilute the effectiveness of AI-generated responses. In this session, we explore cutting-edge techniques to elevate RAG accuracy, including semantic reranking for smarter relevance scoring, Graph RAG to strengthen contextual reasoning through knowledge graphs, and optimized chunking and query transformations to enhance retrieval precision. By implementing these strategies, developers can significantly reduce hallucinations and create AI systems that deliver highly reliable and contextually grounded answers.