In the rapidly evolving field of artificial intelligence, retrieval-augmented generation (RAG) systems have emerged as a powerful tool for enhancing question-answering capabilities by grounding large language models (LLMs) in external knowledge sources. Yet, these systems are not immune to hallucinations—fabricated or inaccurate responses that can undermine trust and utility in enterprise applications. By incorporating reinforcement learning from human annotations, RAG systems can learn to avoid past hallucination patterns. Implications for Industry AdoptionAs RAG systems mature, the focus on hallucination prevention is driving broader AI governance. With continued innovation, RAG could set new standards for trustworthy AI, transforming how businesses leverage generative technologies.