In the rapidly evolving world of artificial intelligence, the development of contextual assistants is a frontier filled with immense promise and substantial challenges. As organizations scale their AI efforts, they encounter several hurdles in training these assistants to understand and respond to diverse and complex inputs effectively. This article explores the key challenges in training AI contextual assistants at scale and the strategies to overcome them, including the use of Retrieval Augmentation Generation (RAG), agent deployment with Langchain, and leveraging various types of Large Language Models (LLMs).