AI Hallucinations: Where does the accountability lie?

I've been hitting a wall with LLM reliability in my current project, specifically regarding "confident hallucinations." I'm seeing a pattern where the model doesn't just get a fact wrong, but constructs a completely fake API endpoint or library method that looks syntactically perfect but doesn't actually exist in the documentation.

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.