The complete guide to Retrieval Augmented Generation (RAG)

Build and deploy a RAG workflow on public clouds with open source tools

This guide provides a detailed reference architecture for creating a RAG workflow using open source tools like OpenSearch and KServe. It also explores the importance of RAG security and data confidentiality, presenting a deep dive into confidential AI.

Contents:

RAG enhanced GenAI reference solution
Building a RAG workflow with open source tools
RAG security and confidentiality (confidential AI and confidential VM in Azure)
How to deploy your RAG pipeline

The guide is intended for data engineers, scientists, and machine learning professionals who want to develop RAG solutions on public cloud platforms like Azure, utilizing enterprise open source tools that are not part of Azure’s native microservices offering. This guide can support various projects, including proofs of concept, development, and production.

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