From RAG to RAG + RAV: A Practical Pipeline for Factual LLM Responses

Recently, I've been working on a project where getting the factual data right was absolutely critical. I’ll be honest, when I first wired up a retrieval-augmented generation (RAG) system, I thought I was mostly done with hallucinations. I had:

• A vector DB full of documents


• A decent embedding model


• A prompt that said "answer only using the context above."

And yet I still got answers that looked grounded but contained subtle factual errors: wrong years, swapped names, invented details that weren't in any source.