Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↑ Price chart
when this has been cheap or pricey

↑ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price Distribution

Price distribution over 60 days • 1 price levels

Days at Price

Price Analysis

Most common price: £45 (60 days, 100.0%)

Price range: £45 - £45

Price levels: 1 different prices over 60 days

Description

Build AI Systems That Actually Work in the Real World Large language models are powerful—but on their own, they are not enough. They hallucinate. They go out of date. They cannot access your data. And when you try to deploy them in production, those limitations become real problems. This book shows you how to solve them. Turn LLMs into Production-Ready Systems Production-Ready RAG Applications is a practical, engineering-focused guide to designing and building scalable AI systems using Retrieval-Augmented Generation (RAG). Instead of treating AI as a black box, this book teaches you how to think like a systems engineer—combining language models with vector databases, embeddings, and retrieval pipelines to create applications that are accurate, reliable, and grounded in real data. You will not just learn what RAG is. You will learn how to build it, optimize it, and deploy it at scale. What You Will Learn How RAG systems overcome the core limitations of standalone LLMs The full architecture of production-grade AI systems Embeddings, vector databases, and semantic search explained clearly Data ingestion, chunking strategies, and indexing pipelines Retrieval techniques, ranking methods, and hybrid search systems How to reduce hallucinations and improve factual accuracy Performance optimization, cost control, and latency trade-offs Real-world system design patterns used in modern AI applications Deployment strategies for scalable, enterprise-ready solutions Who This Book Is For This book is written for: Developers and software engineers building AI-powered applications AI and machine learning engineers who want to move beyond models into full systems Data engineers and architects designing intelligent data pipelines Technical founders building real-world AI products Advanced learners seeking a deeper, practical understanding of modern AI architecture If you already understand the basics of programming or AI and want to go further, this book is for you. From Concept to Production Most AI resources focus on theory or isolated examples. This book focuses on systems that actually run in production. You will learn how to connect models to real data, design efficient retrieval pipelines, and build applications that scale—whether for internal tools, customer-facing systems, or enterprise platforms. Why This Book Matters The future of AI is not just about larger models. It is about building better systems. Retrieval-Augmented Generation is becoming the foundation of modern AI engineering, and the engineers who understand it will shape the next generation of intelligent applications. If you are serious about building AI systems that are reliable, scalable, and production-ready, this book will guide you there.

Product Specifications

Format: hardcover
ASIN: B0GZF1T3MY
Category: Books > Subjects > Computing & Internet > Computer Science > Architecture & Microprocessors
Domain: Amazon UK
Release Date: 03 May 2026
Listed Since: 03 May 2026

Barcode

No barcode data available

It has never been this cheap. We have no record of a lower price.

Production-Ready RAG Applications: Design and Build Scalable LLM Applications Using Vector Databases, Embeddings, and Retrieval-Augmented Generation

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Systems Thinking for Agentic AI: A Software Architect’s Guide to Building Reliable LLM and Agent Systems

Hands-On RAG for Production: Design, Develop, and Deploy Production-Ready RAG Applications

Large Language Models in Finance: A hands-on guide to LLM architectures, agents, RAG, governance, and evaluation in finance

Intermediate Python and Large Language Models

Architectures for the Intelligent AI-Ready Enterprise: Building real-world solutions with MongoDB

Agentic AI Unleashed: A guide to designing, building, and deploying autonomous AI systems (English Edition)

Multi-Agent AI Engineering: Design, build, and operate AI systems that think and act as coordinated teams

From Model to Product: Foundations for Practical AI Engineering

Building Reliable AI Systems

Building Generative AI-Powered Apps: A Hands-on Guide for Developers

Generative AI at AWS: Turn business strategy into production-ready AI applications and agents

Manning Essential GraphRAG - Knowledge Graph-Enhanced RAG Book

Redefining Data Engineering with AI: Agentic Engineering Your Way to Data Democracy

Large Language Models Projects: Apply and Implement Strategies for Large Language Models

Post-training for Foundation Models: Methods, Systems, and Industrial Practices

A Practical Guide to Generative AI Using Amazon Bedrock: Building, Deploying, and Securing Generative AI Applications

Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

The Generative AI Career Masterplan: Navigate the future of AI with practical insights from industry pioneers at AI-first organizations

Empowering the Public Sector with Generative AI: From Strategy and Design to Real-World Applications

Mastering NLP from Foundations to LLMs: Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python

AI Starts with Data: A Practical Blueprint for Trusted, Governed, Enterprise AI

Post-training for Foundation Models: Methods, Systems, and Industrial Practices

THE ARCHITECTURE OF ARTIFICIAL INTELLIGENCE: A Comprehensive Foundation for Understanding and Building Artificial Intelligence Systems

Observability for Large Language Models: Site Reliability and Chaos Engineering for AI at Scale

It has never been this cheap. We have no record of a lower price.

Production-Ready RAG Applications: Design and Build Scalable LLM Applications Using Vector Databases, Embeddings, and Retrieval-Augmented Generation

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Systems Thinking for Agentic AI: A Software Architect’s Guide to Building Reliable LLM and Agent Systems

Hands-On RAG for Production: Design, Develop, and Deploy Production-Ready RAG Applications

Large Language Models in Finance: A hands-on guide to LLM architectures, agents, RAG, governance, and evaluation in finance

Intermediate Python and Large Language Models

Architectures for the Intelligent AI-Ready Enterprise: Building real-world solutions with MongoDB

Agentic AI Unleashed: A guide to designing, building, and deploying autonomous AI systems (English Edition)

Multi-Agent AI Engineering: Design, build, and operate AI systems that think and act as coordinated teams

From Model to Product: Foundations for Practical AI Engineering

Building Reliable AI Systems

Building Generative AI-Powered Apps: A Hands-on Guide for Developers

Generative AI at AWS: Turn business strategy into production-ready AI applications and agents

Manning Essential GraphRAG - Knowledge Graph-Enhanced RAG Book

Redefining Data Engineering with AI: Agentic Engineering Your Way to Data Democracy

Large Language Models Projects: Apply and Implement Strategies for Large Language Models

Post-training for Foundation Models: Methods, Systems, and Industrial Practices

A Practical Guide to Generative AI Using Amazon Bedrock: Building, Deploying, and Securing Generative AI Applications

Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

The Generative AI Career Masterplan: Navigate the future of AI with practical insights from industry pioneers at AI-first organizations

Empowering the Public Sector with Generative AI: From Strategy and Design to Real-World Applications

Mastering NLP from Foundations to LLMs: Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python

AI Starts with Data: A Practical Blueprint for Trusted, Governed, Enterprise AI

Post-training for Foundation Models: Methods, Systems, and Industrial Practices

THE ARCHITECTURE OF ARTIFICIAL INTELLIGENCE: A Comprehensive Foundation for Understanding and Building Artificial Intelligence Systems

Observability for Large Language Models: Site Reliability and Chaos Engineering for AI at Scale