Deep explanation
Azure AI failure in an enterprise RAG assistant: how would you respond?
Mid-level RAG incident question: trace ungrounded answers in an Azure OpenAI plus Azure AI Search assistant to retrieval, index freshness, model version changes, or prompt issues, then fix with measurable groundedness checks.
Quick answer
I would first find out whether the problem is retrieval or generation by looking at what chunks the model actually received for bad answers. If the right chunks were not retrieved, I check the Azure AI Search index, indexer runs, and query settings. If the right chunks were there but the model ignored them, I check whether the model version behind the deployment changed and tighten the prompt and citation rules, then confirm with a groundedness evaluation before closing the incident.
The interview question
You are on call for an enterprise RAG assistant built on Azure OpenAI and Azure AI Search. Users report that answers are less grounded: they cite the wrong documents or add facts that are not in the sources. Nothing was deployed by your team this week. How would you investigate and fix this without blowing the latency SLA or the token budget?
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