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 to Production-Scale AI Systems - Softcover

Ramteke, Suraj

9798183764093: 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 to Production-Scale AI Systems

Softcover

ISBN 13: 9798183764093

Publisher: Independently published, 2026

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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:
Most AI books show you how to call an API. This book shows you how to build the entire system around it — the RAG pipeline that actually retrieves the right content, the agent that recovers from tool failures, the FastAPI deployment with monitoring and cost controls, and the evaluation framework that tells you whether your system is getting better or worse.

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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