Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) are redefining how software systems are built. But most resources either focus on theory — or on shallow demos.

This book bridges the gap.

Building LLM Systems with RAG takes you from Machine Learning fundamentals to deploying scalable, production-ready Generative AI systems using modern tools like LangChain and Ollama.

This is not just another prompt engineering guide.

This is a system-building handbook.

What You’ll Learn

You will build a complete mental model of modern AI systems:

Foundations of Machine Learning and Deep Learning
Neural Networks, Transformers, and LLM architecture
Prompt Engineering techniques used in real systems
How RAG reduces hallucinations and improves reliability
Embeddings and vector databases
Chunking strategies that impact retrieval quality
Hybrid search (Sparse + Dense retrieval)
Reranking techniques for precision
Evaluating RAG systems properly
Designing production-ready LLM pipelines
Deploying scalable RAG systems using LangChain and Ollama
Running Local AI models securely and cost-effectively

By the end of this book, you won’t just understand LLMs — you’ll know how to build reliable AI systems around them.

Who This Book Is For

This book is for:

Software Engineers
Machine Learning Engineers
AI Architects
Technical Founders
Developers moving into Generative AI

You must know Python not Perfessional but minimum syntax understanding.

No PhD required — but curiosity and technical mindset are essential.

From Deep Learning to Production

You will move step-by-step:

Machine Learning
→ Deep Learning
→ Transformers
→ Large Language Models
→ Prompt Engineering
→ Basic RAG
→ Advanced RAG
→ Production Deployment

Each concept builds toward one goal:

Creating scalable, production-grade LLM systems.

What Makes This Book Different?

Unlike many AI books:

It focuses on systems, not just models
It explains why architectural decisions matter
It includes production engineering considerations
It combines theory with practical design
It uses real-world RAG pipelines
It integrates LangChain and Ollama for local AI

This book prepares you for the real world — not just the demo environment.

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Building LLM Systems with RAG

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