LLM Engineering in Practice: Bridging Theory and Practical Application

In LLM Engineering in Practice: Bridging Theory and Practical Application, we take a comprehensive journey through the full lifecycle of large language models — from core architectural foundations to real-world intelligent system deployment. Whether you're an AI engineer looking to sharpen your production skills or a technical leader navigating enterprise LLM adoption, this book gives you the battle-tested roadmap you need. Authored by a practitioner with experience serving over 60 million users, every concept is grounded in real industrial-scale deployment.

The guide starts by laying the groundwork with a bottom-up overview of LLMs, exploring the core Transformer components and architectural decisions — including attention optimizations like GQA, MQA, and MLA, as well as Mixture-of-Experts and long-context modeling techniques — that separate research prototypes from production-grade systems.

As we progress, the guide walks you through the complete training pipeline. You'll master data preparation for pretraining, SFT, and RLHF, before diving into fine-tuning techniques ranging from full-parameter training to parameter-efficient methods like LoRA. Advanced reinforcement learning algorithms including PPO, DPO, GRPO, and DAPO are covered in depth, alongside multimodal training strategies that extend LLMs to images and video. The guide then transitions into deployment — covering quantization, pruning, and knowledge distillation — before culminating in two flagship application domains.

For intelligent RAG systems, you'll learn to build end-to-end retrieval pipelines spanning query understanding, index construction, hybrid recall combining BM25 and dense embeddings, re-ranking, and response generation — all designed for responsible, hallucination-aware production deployment.

For AI agents, you'll explore three agent frameworks — ReAct, Plan-Execute, and ReCode — alongside memory management, function calling, tool use, and a complete agent training pipeline from SFT initialization to reinforcement learning. You'll also tackle the real-world challenges of multi-agent coordination, safety, and ethical alignment.

This guide is designed to be hands-on, offering practical insights drawn from large-scale industrial deployments. Throughout, you'll build expertise with key tools including DeepSpeed, vLLM, VERL, Hugging Face Accelerate, and more. By the end, you'll have the skills and confidence to build, fine-tune, deploy, and scale LLM systems across real-world production environments.