AI reliability issues extend beyond hallucinations to include poor data quality, drift in embedding space, confused context, output sensitivity, and the balance of human involvement in processes. Ensuring the reliability of AI applications requires meticulous attention to data integrity, retrieval systems, and evaluation methods, rather than solely focusing on the model's performance. Building trust in AI involves comprehensive monitoring across all layers of the AI system.