RAG in Oracle Database 23ai combines vector search with LLMs to enhance responses by retrieving
relevant private data from the database (e.g., via VECTOR columns) and augmenting LLM prompts.
This (A) improves context-awareness and precision, leveraging enterprise-specific data without
retraining LLMs. Optimizing LLM performance (B) is a secondary benefit, not the core focus. Training
specialized LLMs (C) is not RAG’s purpose; it uses existing models. Real-time streaming (D) is possible
but not the primary benefit, as RAG focuses on stored data retrieval. Oracle’s RAG documentation
emphasizes private data integration for better LLM outputs.
Reference:Oracle Database 23ai AI Vector Search Guide, Chapter on RAG.
