AI Hallucinations: Where does the accountability lie?

A user reports that AI models often enter a 'hallucination loop' where feeding an error message back into the prompt results in a second, equally fake solution that incorporates the error. The user questions whether this is a base model failure or a prompt engineering issue, and seeks ways to implement a validation layer to catch hallucinations before production deployment.

AI Hallucinations: Where does the accountability lie? The technical frustration isn't just the wrong answer—it's the "hallucination loop." When I feed the error message back into the prompt, the AI often apologizes and then provides a second, equally fake solution that somehow incorporates the error message into its logic. From a deployment perspective, this raises a massive question about the AI workflow: is this a failure of the base model, or is it a prompt engineering failure on my end? If I'm using a RAG Retrieval-Augmented Generation setup and the model still ignores the provided context to hallucinate a "better" answer, the system becomes a liability rather than a tool. I'm trying to figure out if there's a way to force a "hard stop" when the model hits a confidence threshold. Right now, the output is binary: it either gives an answer or it doesn't, but it rarely says "I don't know based on the provided data." If you've managed to implement a reliable validation layer to catch these hallucinations before they hit production, I'd be interested in seeing how you structured the verification step. Next Construction Estimation: Why Bids Fail → /en/threads/2268/