Deep explanation
OpenAI failure in an enterprise RAG assistant: how would you respond?
Junior RAG incident question: debug ungrounded answers in an OpenAI file_search assistant by inspecting retrieved results, vector store contents, model alias changes, and prompt rules, then verify with a small eval.
Quick answer
I'd start by looking at what the file_search tool actually returned for the bad answers, because that tells me if the problem is search or the model. If the right file wasn't returned, I check the vector store for missing, failed, or duplicate files and the search settings. If the right content was returned but ignored, I check whether the model changed under an alias and tighten the instructions, then test the fix on a small set of real questions before rolling it out.
The interview question
You are the junior engineer on call for an internal RAG assistant built on the OpenAI API: documents are stored in an OpenAI vector store and searched with the file_search tool, and answers come from a GPT model. Employees report that answers are less grounded than before: they cite the wrong file or state things the documents don't say. How would you investigate and fix it without making replies slower or more expensive?
Deep explanation
Simple explanation
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