RAG from Scratch

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.

• 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.

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.

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