Description
This book is a practical guide to understanding and preparing for large language model (LLM) engineering roles, focusing not just on theory but on how LLMs work as complete systems in real-world applications. It walks through the full stack—from tokenization, embeddings, and attention to retrieval (RAG), evaluation, deployment, and optimization—while emphasizing how these components interact to deliver reliable, scalable AI products. Rather than memorizing concepts, the book trains readers to think like engineers: framing problems, making trade-offs between cost, latency, and accuracy, and explaining design decisions clearly in interviews. It also covers common patterns like hybrid search, reranking, and context management, along with career guidance such as resume building and communicating impact, ultimately aiming to help readers bridge the gap between LLM theory and production-ready systems.
