AI Forward Deployed Engineer Interview Questions
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Q29IntermediateSystem design

Design a workflow for customer SMEs to label data and review AI outputs during an engagement.

30-second answerSay your answer out loud first, then reveal.
Historical and live pilot cases go through a sampler, are assigned to SMEs with overlap, and are reviewed in a review UI; the resulting labels, corrections and reason codes feed agreement metrics, eval set versions and few-shot or fine-tune data, and the eval set drives a quality dashboard shared with the customer.

Design details

  • Rubric with reason codes ("missed clause", "wrong date", "hallucinated", "tone") makes errors analysable.
  • Inline correction captures the correct output, not just "wrong".
  • Smart sampling: prioritise low-confidence, new categories and user-flagged outputs.
  • Low friction: keyboard shortcuts, small batches (20 cases per session), embedding the UI in the tools SMEs already use where possible.
  • Quality control: inter-annotator agreement; adjudication for disagreements; periodic recalibration sessions.
  • Security: SMEs see only data they're allowed to; audit who labelled what.
  • Motivation: show weekly charts of "accuracy improved from 78% to 89% thanks to your reviews."
PreviousDesign how you'd integrate an LLM workflow into a customer's existing enterprise systems (e.g. Salesforce, ServiceNow, SAP).

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