Overview

The third volume of A Practitioner's Journey. Twenty-one chapters cover the modern AI stack end to end - large language models, retrieval-augmented generation, RLHF alignment, autonomous agents, and the LLM-specific infrastructure that surrounds them. This is not another ""build a ChatGPT clone"" book. It is a working engineer's guide to the techniques and engineering decisions behind production AI systems. You will start with NLP foundations and tokenization, move through transformer architectures and LLMs, build production-grade RAG pipelines with vector stores and reranking, train reward models and align LLMs with RLHF and DPO, and finish with autonomous agents, the Model Context Protocol (MCP), multi-agent coordination, and multi-modal models. You will learn to: Build and deploy production RAG with vector stores, reranking, and citation grounding Fine-tune and align LLMs with RLHF, DPO, and Constitutional AI Design autonomous agent loops with safe tool use and approval gates Coordinate multiple agents through the Model Context Protocol (MCP) Right-size LLM infrastructure across Bedrock, Vertex, Groq, and on-prem Distill large models into deployable, edge-ready footprints Companion volumes: Books 1 and 2 of A Practitioner's Journey cover classical ML, deep learning, and the production engineering stack - recommended as prerequisites if you are new to the field, optional if you already work in ML/AI.

Full Product Details

9798257184468

Table of Contents

Reviews

Author Information

Tab Content 6

Countries Available