Hallucinations in large language models are confident but factually incorrect, nonsensical, or ungrounded responses—a fundamental challenge that emerges from the probabilistic nature of token-by-token prediction in transformer architectures. Preventing hallucinations requires grounding outputs in verifiable sources, constraining generation behavior, and implementing multi-layered verification rather than relying solely on the model's training. The key insight: effective hallucination prevention is an orchestration problem, combining prompt design, retrieval mechanisms, sampling strategies, and post-generation validation into a coherent system where each layer compensates for the others' weaknesses.