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Artificial Intelligence in Production: A Practitioner's Journey - From Transformers and Large Language Models to Autonomous Agents
Paperback
Series: The Practitioner's Journey, Book 3
Publisher: Independently Published
Published: Apr 13 2026
Pages: 552
Weight: 2.08
Height: 1.12 Width: 7.00 Depth: 10.00
Language: English
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.
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