Data Engineering for RAG: Building Reliable AI with Better Data Pipelines | Towards AI

Author(s): GOWRI SHANKAR RAJU

Originally published on Towards AI.

Introduction

Retrieval-Augmented Generation (RAG) is a practical method for connecting Large Language Models to enterprise information. Rather than relying solely on training data, RAG retrieves relevant information from company documents, databases, knowledge bases, and other business sources before generating a response.

While the concept is straightforward, production RAG systems face significant challenges because enterprise data is often unclean, inconsistent, or unprepared for AI. Information is typically distributed across PDFs, APIs, cloud storage, internal portals, databases, support systems, and collaboration platforms. Documents may be outdated, duplicated, poorly structured, or lack essential metadata.