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Self-Hosted RAG: Secure and Flexible AI Knowledge Systems

Retrieval-Augmented Generation (RAG) has emerged as a solution to overcome the limitations of large language models (LLMs), specifically their limited context memory and dependence on static training data. RAG combines LLMs with external knowledge sources to generate more accurate, contextual, up-to-date answers.

This whitepaper covers the fundamentals of RAG, concerns with security and compliance of cloud-hosted deployments, and how self-hosted or hybrid RAG architecture emerges as a solution. Each deployment architecture is explained, along with its characteristics, benefits, and trade-offs, to guide organizations in choosing the suitable deployment model.