Local Generative AI Engineering
Build, Optimize & Ship AI Systems Locally (LLMs, RAG)
Artificial Intelligence is no longer a distant concept—it is already part of everyday life. Millions of people use AI-powered tools daily. However, while most users know how to interact with AI, very few understand how AI systems are actually built.
Local Generative AI Engineering is written to close this gap.
This book is not about surface-level tutorials or cloud-only shortcuts. Instead, it is a practical, engineering-driven guide that teaches you how to design, build, and deploy real Generative AI systems on your own machine, with clarity, control, and confidence.
Why This Book Exists
Today’s learners are surrounded by confusing terminology—Artificial Intelligence, Machine Learning, Deep Learning, Large Language Models, Generative AI, embeddings, vector databases, and RAG systems. Many resources either remain overly theoretical or jump straight into tools without explaining the reasoning behind system design.
As a result, a large implementation gap has emerged.
Many learners:
Know Python
Understand basic AI or ML concepts
Can follow tutorials
Yet struggle when asked to build a system that can:
Understand documents
Retrieve relevant information
Generate accurate, context-aware responses
This book exists to bridge that implementation gap—by focusing on fundamentals, architecture, and hands-on system building.
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RAHUL SHARMA
The author is an AI educator who documents the learning journey by transforming acquired knowledge into a structured, beginner-friendly format. The work focuses on simplifying complex concepts so they become clear, practical, and accessible to a wide range of learners.
