Question 1

How does enterprise RAG work?

Accepted Answer

Enterprise RAG typically combines document ingestion, hybrid retrieval (lexical and vector), metadata filtering, optional reranking, grounded generation, and evaluation. Permissions should apply at retrieval time, and low-confidence cases should escalate when risk warrants it.

Question 2

How do you reduce AI hallucinations?

Accepted Answer

Improve retrieval quality, require grounded answers, refuse when context is insufficient, evaluate failure cases, and escalate low-confidence outputs to humans when risk warrants it. No production technique eliminates hallucinations entirely.

Question 3

What is AI confidence scoring?

Accepted Answer

AI confidence scoring is a signal — derived from retrieval strength, model uncertainty estimates, policy checks, or evaluation heuristics — used to decide whether an answer can be shown, needs citation, or must escalate to a human. It is a decision aid, not a guarantee of correctness.

Question 4

When should an enterprise use RAG?

Accepted Answer

Use enterprise RAG when answers must be grounded in organization-controlled documents or data that change over time, and when those sources can be permissioned, evaluated, and observed in production. Skip RAG when a deterministic query or a narrow workflow is enough.

Question 5

RAG vs fine-tuning — which should an enterprise choose?

Accepted Answer

Prefer RAG when knowledge changes often and must stay permissioned and citeable. Prefer fine-tuning when you need stable style or task behavior that retrieval alone cannot provide. Many production systems combine both: retrieval for facts, fine-tuning or adapters for format — with evaluation either way.

Enterprise RAG

What is enterprise RAG?

Case Studies

MCP in Production: Architecting Narrow Data Channels for LLMs

Original Vasiva decision assets

Enterprise AI Readiness Framework

Enterprise RAG Production Checklist

About Enterprise RAG

Need retrieval that can survive production?