Day One and Beyond - Private RAG for Confidentiality and Security @oracledevs
Day One and Beyond - Private RAG for Confidentiality and Security  @oracledevs
Uploaded July 2026 | Updated September 2026, 3 weeks ago
Traditionally, Retrieval Augmented Generation (RAG) would use a public Large Language Model (LLM) to generate an answer from curated facts from your vector database. Although this approach works, it has the side effect of leaking your questions and context data to the public LLM. Some providers of LLMs use your questions and context data from their training, and they may even sell your data to other companies.
A more secure approach is to use a local LLM which you have complete control over. This means that you never leak your data to a third party. Most people are not experienced in managing local LLMs with the security, privacy, availability and scalability needed for enterprise applications. To enable the deployment of enterprise ready local large language models, Oracle created the LLM Service for the Oracle Private AI Services Container. This session will cover what the LLM Service of the Private AI Services Container is and how Oracle AI Vector Search and SelectAI use it for private RAG.

Self-Study Links:
Product Page: What is RAG? - oracle.com/artificial-intelligence/generative-ai/retrieval-augmented-generation-rag
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Day One and Beyond - Private RAG for Confidentiality and Security

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