Problem
Scenario
You use LLMs as part of your day-to-day analytics work, for example to speed up SQL drafting, summarize findings, or turn messy business questions into structured analysis plans. The value is real, but so are the risks when the model sounds confident and is wrong.
Question
How do you currently use LLMs in your analytics workflow, and how do you make that usage reliable enough for real analysis work?
What this tests
- Prompt design for analytics tasks
- LLM evaluation in real workflows
- Hallucination control
- Structured extraction and schema-aware outputs
Practicing as: AI Engineer interview at PwCHi, I'll play your PwC interviewer for the AI Engineer role. Candidates describe these interviews as often stressful and moderately difficult, so expect me to be direct and to the point. Take your time with the question above and answer like we're in the room.
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