In late 2023, the transition from the optimistic "AI Summer" to the more pragmatic "AI Autumn" is marked by the challenges of moving Large Language Model (LLM) prototypes to full-scale enterprise adoption. While new LLM development frameworks simplify initial prototype creation, they fall short in providing the necessary features for reliable, scalable, and auditable workflows required by operational teams. Apache Airflow® emerges as a crucial tool in addressing these gaps, enabling day-2 operations by facilitating the orchestration of complex workflows that integrate LLMs, vector databases, and other development frameworks. The blog highlights the importance of Airflow's capabilities, such as atomicity, error handling, data refill, scheduling for freshness, and logging, which are essential for managing the dynamic and iterative nature of RAG-based LLM applications. By enabling better scaling, integration, and governance, Airflow not only helps prototype LLM applications but also supports their transition to enterprise-grade solutions.