Foundation Model Engineering: From Theory to Production
This article introduces a technical textbook titled 'Foundation Model Engineering' designed for AI engineers and researchers. It aims to bridge the gap between theoretical concepts and practical production systems in the field of large language models.
Why it matters
As AI integration grows, there is a critical need for standardized engineering practices to manage the complexity of modern foundation models.
Foundation Model Engineering is a technical textbook for readers who want to understand how modern foundation models actually work, why the stack evolved the way it did, and what engineering trade-offs appear when those ideas meet real systems.
This project is written primarily for AI engineers and research-oriented readers who want to move past surface-level API usage and build a deeper mental model of architectures, training pipelines, inference systems, retrieval stacks, evaluation loops, and agentic workflows.
The goal is not to provide scattered tips or isolated definitions. The goal is to explain the historical flow, mathematical ideas, and systems constraints that connect topics like attention, MoE, RLHF, multimodality, long-context serving, RAG, and agents into one engineering narrative.
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