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Shipping AI to Production: The Complete Engineer's Guide to LLMs, RAG, Agents, Fine-Tuning & Real-World Deployment 5-in-1 Mastery: From First Prompt t
Paperback
Series: AI Engineering for Developers, Book 1
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Publisher: Independently Published
Published: Jun 22 2026
Pages: 350
Weight: 1.43
Height: 0.91 Width: 6.00 Depth: 9.00
Language: English
Are you building AI systems that work in demos but break in production?
This is the book that bridges the gap - written by an engineer who has shipped real AI systems for logistics, healthcare, and enterprise clients in India and internationally.
What makes this different from every other AI book:
Every concept is taught through working production Python code - not toy examples. You will find implementations of hybrid search with Qdrant, LoRA fine-tuning with Unsloth, multi-agent orchestration with LangGraph, streaming APIs with FastAPI and WebSockets, and security defense-in-depth for prompt injection.
What you will build across 27 chapters:
Chapter 1-4 cover the foundations that most books skip: how tokenization actually works, why RAG retrieves the wrong chunks, what temperature really does to token probabilities, and how to prevent hallucination systematically.
Chapters 5-6 build production AI agents from scratch - the ReAct loop, tool design, agent memory, multi-agent supervisor-worker patterns, and parallel Map-Reduce processing.
Chapters 7-9 cover production engineering: a full FastAPI streaming API, Docker deployment, Prometheus monitoring, content safety pipelines, and a semantic cache that cuts API costs by 40-70%.
Chapters 10-15 cover what's actually trending in 2026: Model Context Protocol (MCP), LangGraph state machines, Qdrant vector database operations, multimodal AI, and enterprise AI gateway architecture.
Chapters 16-22 present real case studies - logistics, legal, healthcare, e-commerce - plus complete implementations of document processing, customer support automation, code review agents, vector search engineering, and AI product metrics.
Chapters 23-27 cover evaluation frameworks, testing AI systems, security at depth, scalability from 100 to 10 million requests, and a patterns reference handbook with 8 complete production patterns.
This book is for you if:
- You are a Python engineer who wants to ship AI features to production
- You have called the Claude or OpenAI API but want to build real systems around it
- You are preparing for AI engineering interviews or roles in 2026
- You work in India's growing AI engineering market and want practical, job-ready skills
Includes:
- 150+ production-ready Python code examples
- Real case studies from Indian industry (logistics, legal, healthcare, e-commerce)
- 27 chapters + 3 appendices + troubleshooting reference
- Exercises for every chapter with clear success criteria
- Career guide with 90-day learning plan and salary ranges for 2026
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