RAG from Scratch: Building Robust Retrieval-Augmented Generation Systems

By Thompson Carter

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 For

This 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 Systems

Pure 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

• Format: Paperback
• Language: English
• ISBN: B0GK8NMLFS
• ISBN13: 9798242344303
• Release Date: January 2026
• Publisher: Independently Published
• Length: 254 Pages
• Weight: 0.76 lbs.
• Dimensions: 0.5" x 6.0" x 9.0"