This episode introduce the rapidly evolving field of AI engineering, driven by the emergence of powerful foundation models. The text explains how the increasing scale of these models, trained with self-supervision on vast datasets, has unlocked new AI capabilities and applications. It details the architecture and training of foundation models, covering pre-training, supervised finetuning, and preference learning to align them with human expectations. The discussion extends to practical considerations for building AI applications, such as use case identification, planning, and the AI engineering stack, highlighting the shift from traditional machine learning. Finally, the text explores challenges like inconsistency and hallucinations inherent in the probabilistic nature of these models and various sampling strategies to influence their output
