How Would You Add AI to Our App?

This is THE question for this posting. The agency is building an AI-assisted transcript-review service, and they want to know whether you'll bring judgment or hype. Rehearse this one until the structure is automatic: clarify the use case → pick a pattern → pilot small → guard it.

"We're modernizing our transcript-review process. If we brought you on, how would you add AI to our application?"

Start from the use case, not the model

The strong candidate's first move is a clarifying question, not a technology: "What decision or task are we trying to speed up, and what happens today when it's done wrong?" For transcript review, the answer is concrete: analysts compare candidate coursework against Subject Matter Requirements (SMRs), and errors affect someone's credential. That framing does two things — it tells you the AI's job is decision support, not decision making, and it tells you the error cost is high, so the design must be fail-closed with a human-in-the-loop.

Then reason aloud through a pattern menu instead of reaching for one hammer:

• Extraction — turn scanned transcripts into structured course records (name, units, grade).
• Classification / matching — score each course against SMR criteria with evidence.
• RAG chatbot — let staff ask questions over policy manuals with citations.
• Copilot — draft the analyst's summary; the analyst edits and signs.

For this job, extraction plus evidence-backed matching is the core; the chatbot is a nice second project. Saying that ordering out loud shows prioritization.

Pilot small, measure, then widen

Propose a thin slice: one credential area, a golden set of transcripts already reviewed by staff, and an offline evaluation comparing AI suggestions to those human determinations before anything touches production. Define the metric with the interviewer — agreement rate, false-met rate (the dangerous one), analyst time saved. Ship behind a flag, log every suggestion and every override, and let the override rate tell you when trust is earned.

Close the answer with guardrails: confidence thresholds that route low-certainty items straight to a human, structured outputs validated against a schema, and an audit trail — because a state agency will be asked to explain any determination.

Red flags

• Jumping straight to "I'd fine-tune a model on your data" — expensive, slow, and usually the wrong tool before retrieval and prompting are exhausted.
• Proposing the AI make final determinations. In this domain, HITL is mandatory, not optional.
• No mention of evaluation — "it seemed to work in the demo" is not an answer a state agency can take to an auditor.
• Ignoring cost, latency, and data-sensitivity questions entirely.

Practice prompts

1. Deliver the full answer aloud in under three minutes, using the transcript service as your running example.
2. The interviewer says budget is tight — which single pattern do you pilot first, and why?
3. Explain to a non-technical program manager why the AI should suggest rather than decide.