Building LLM Systems with RAG (Paperback)

Ali Jafari

ISBN 13: 9798250073844

Editore: Independently Published, 2026

Riguardo questo articolo

Paperback. 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 LearnYou will build a complete mental model of modern AI systems: Foundations of Machine Learning and Deep LearningNeural Networks, Transformers, and LLM architecturePrompt Engineering techniques used in real systemsHow RAG reduces hallucinations and improves reliabilityEmbeddings and vector databasesChunking strategies that impact retrieval qualityHybrid search (Sparse + Dense retrieval)Reranking techniques for precisionEvaluating RAG systems properlyDesigning production-ready LLM pipelinesDeploying scalable RAG systems using LangChain and OllamaRunning Local AI models securely and cost-effectivelyBy 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 ForThis book is for: Software EngineersMachine Learning EngineersAI ArchitectsTechnical FoundersDevelopers moving into Generative AIYou must know Python not Perfessional but minimum syntax understanding.No PhD required - but curiosity and technical mindset are essential.From Deep Learning to ProductionYou will move step-by-step: Machine Learning Deep Learning Transformers Large Language Models Prompt Engineering Basic RAG Advanced RAG Production DeploymentEach 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 modelsIt explains why architectural decisions matterIt includes production engineering considerationsIt combines theory with practical designIt uses real-world RAG pipelinesIt integrates LangChain and Ollama for local AIThis book prepares you for the real world - not just the demo environment. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Codice articolo 9798250073844

Dati bibliografici

• Titolo: Building LLM Systems with RAG (Paperback)
• Editore: Independently Published
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 13: 9798250073844
• Rilegatura: Paperback
• Condizione: new

Descrizione articolo

Riassunto

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.