Overview

Great AI answers start with the right data, not bigger models. Large language models are powerful-but unreliable when they operate without context. Retrieval-Augmented Generation (RAG) solves this by grounding AI responses in real, trusted data. This book shows how to build robust RAG systems from the ground up, focusing on reliability, accuracy, and production readiness. RAG from Scratch is a practical guide for developers and architects who want to move beyond toy demos and design RAG pipelines that work in real-world systems. What You'll Learn in This Book Core principles behind Retrieval-Augmented Generation Designing end-to-end RAG architectures Chunking, embedding, and indexing strategies Using vector databases for efficient retrieval Prompting LLMs with retrieved context Evaluating relevance, accuracy, and latency Hardening RAG systems for production environments The focus is on system design and robustness, not surface-level examples. Who This Book Is ForThis guide is ideal for: Software engineers building AI-powered applications AI and ML engineers working with LLMs Data engineers supporting knowledge systems Architects designing enterprise AI platforms Teams deploying RAG in production Basic programming experience and familiarity with LLM concepts are recommended. Why RAG Is Essential for Real AI SystemsPure LLMs generate text. RAG systems retrieve knowledge and reason over it. Well-designed RAG systems: Reduce hallucinations Improve factual accuracy Enable enterprise data integration Support auditability and updates This book teaches how to engineer RAG systems you can trust. Build RAG Systems That Hold Up in Production

Full Product Details

9798242344303

Table of Contents

Reviews

Author Information

Tab Content 6

Countries Available