Paul Iusztin shares his journey into AI engineering and LLMs, highlighting the shift from traditional model fine-tuning to utilizing foundational models with a focus on prompt engineering and Retrieval-Augmented Generation (RAG). He emphasizes the importance of a structured architecture in AI applications, comprising distinct layers for infrastructure, models, and applications, as well as a feature training inference framework for efficient system design.

POML (Prompt Orchestration Markup Language) is a structured markup language designed to enhance prompt engineering for Large Language Models (LLMs) by addressing issues like format sensitivity and data integration. It features an HTML-like syntax, comprehensive data handling, and a templating engine, facilitating the creation of modular, maintainable prompts. The toolkit also includes IDE support and SDKs for various programming languages, streamlining the development process for LLM applications.