Artificial Intelligence in Production: A Practitioner's Journey - From Transformers and Large Language Models to Autonomous Agents

By: Dharmalingam, Krishna

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 groundingFine-tune and align LLMs with RLHF, DPO, and Constitutional AIDesign autonomous agent loops with safe tool use and approval gatesCoordinate multiple agents through the Model Context Protocol (MCP)Right-size LLM infrastructure across Bedrock, Vertex, Groq, and on-premDistill 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.

• Format: Paperback
• Language: English
• ISBN: B0GX1R3QCQ
• ISBN13: 9798257184468
• Release Date: April 2026
• Publisher: Independently Published
• Length: 552 Pages
• Weight: 2.08 lbs.
• Dimensions: 1.1" x 7.0" x 10.0"