Building A large language model with Ai: A Practical Guide to Structuring LLM Systems from Scratch Using Reverse-Engineering Techniques

Name: Building A large language model with Ai: A Practical Guide to Structuring LLM Systems from Scratch Using Reverse-Engineering Techniques
Brand: Independently Published
SKU: 9798246060261
Price: 1806.0 INR
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by Philip Steph

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ISBN13: 9798246060261
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: January 28, 2026
Pages: 174
Original Price: USD 19.99
Language: English
Edition: N/A
Item Weight: 418 grams
BISAC Subject(s): Robotics

About The Book

Artificial intelligence isn't magic. It's architecture. And this book shows you how to build it.

Building A Large Language Model with AI is a hands-on, practical guide for developers, engineers, researchers, and ambitious learners who want to understand what's really happening under the hood of modern language models - and how to design their own systems from the ground up.

Instead of treating large language models like mysterious black boxes, this book breaks them apart, studies how they work, and shows you how to reconstruct powerful LLM systems using smart reverse-engineering strategies and modern AI design principles.

Whether your goal is to build smarter AI products, understand how generative systems are structured, or move from "AI user" to AI builder, this guide walks you through the full system-thinking approach behind today's most advanced language technologies.

Inside, you'll learn how to:

Understand the core building blocks behind large language models
Design LLM system architectures that go beyond just training a model
Structure data pipelines that feed intelligent AI systems
Apply reverse-engineering techniques to analyze and replicate advanced AI behaviors
Connect models with memory, tools, and external knowledge sources
Optimize performance, scalability, and real-world usability
Move from theory to deployable AI system design

This is not just another machine learning theory book. It focuses on systems, not just models - showing how components like embeddings, vector databases, prompt pipelines, and reasoning layers work together to create intelligent behavior.

Written in a clear, practical style, this book bridges the gap between academic AI concepts and real-world implementation, making it ideal for:

Software engineers expanding into AI
Machine learning practitioners who want deeper system knowledge
Technical founders building AI-powered products
Students preparing for the next generation of AI careers

If you've ever wondered how large language models actually function at a systems level - and how you can build, structure, and refine your own - this book gives you the blueprint.

The future of AI belongs to builders. This is your starting point.

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