Building LLM Systems with RAG: From Deep Learning to Scalable Generative AI in Production with LangChain and Ollama - Tapa blanda

Sinopsis

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.

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.

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.

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.

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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