RAG for enterprise AI: frequently asked questions

Enterprise leaders evaluating RAG tend to ask the same questions. Here are plain-language answers.

Does RAG eliminate hallucinations?

No. RAG reduces them by grounding answers in retrieved passages, but the model can still misread a passage or stitch unrelated chunks together. Pair RAG with citations and evaluation so users and reviewers can verify.

How is RAG different from fine-tuning?

Fine-tuning changes the model's weights to shift style or embed stable knowledge. RAG leaves the model alone and injects fresh facts at query time. Most enterprises need RAG first — it's cheaper, auditable, and updates the moment a document changes.

Do we need a vector database?

Usually yes for semantic search, but pair it with a keyword index. Codes, part numbers, and legal terms often need exact matches that embeddings miss.

How often should the index refresh?

Match it to the source's change rate. Policies may refresh nightly; a pricing sheet may need near-real-time. Stale indexes are a top cause of wrong answers.

Can RAG keep sensitive data isolated?

Yes, when designed correctly. Metadata filters and tenant isolation should run before retrieval, not after. See data isolation for enterprise LLMs.

How do we measure if RAG is working?

Measure retrieval and generation separately. Track recall@k for retrieval and faithfulness plus answer correctness for generation. Full guide: how to reduce AI hallucinations in enterprise answers.

Who owns a RAG system once it's live?

A cross-functional team: IT for infrastructure, data owners for source quality, and an AI leader for policy. A Fractional Chief AI Officer can hold this together without a full-time hire.