This collection of excerpts offers a detailed exploration of AI Engineering, focusing on the practical aspects of building and deploying applications using foundation models. It discusses the evolution of AI models, from traditional ML to large language and multimodal models, outlining their capabilities and the challenges they present. The text covers key areas like evaluation methodologies, different model architectures and scaling factors, prompt engineering best practices for guiding model behavior, and advanced techniques such as Retrieval Augmented Generation (RAG) and the use of agents. Furthermore, it addresses crucial considerations for production systems, including finetuning models, dataset engineering (curation, synthesis, and processing), inference optimization for efficiency, and designing robust AI engineering architectures that incorporate feedback mechanisms.

AI Engineering with Foundation Models - https://amzn.to/3YMqoS9