While public generative AI models are impressive, enterprise adoption requires strict privacy guarantees. Retrieval-Augmented Generation (RAG) enables organizations to connect foundational models directly to internal documentation, wikis, and databases while ensuring data never leaves confidential VPC parameters.
Why RAG Outperforms Simple Fine-Tuning
RAG pipelines dynamically query vectorized embeddings from systems like Pinecone or Milvus at runtime. This provides hallucination-free, citation-backed answers with strict user permission access controls.