Interview Preparation — Quick Reference#

Interview Questions for You to Ask#

Asking strong questions signals principal-level thinking:

About the AI systems:

• "What is the scale of your current AI workloads? Tokens per day? Concurrent users?"
• "What is the PHI surface area of your AI systems? How do you handle HIPAA compliance today?"
• "How do you detect quality regressions before users notice?"

About the team and platform:

• "How do product teams consume AI capabilities — directly via API, or through a platform layer?"
• "Who owns evaluation? Is it on the model team, the product team, or the platform team?"

About the role:

• "What does success look like for this role in the first 90 days?"
• "What architectural decision that was already made would you reconsider if you could?"

Common Interview Questions — One-Line Answers#

"What is RAG?" Retrieve relevant documents at inference time and inject them into the LLM prompt — grounds generation in current, organization-specific knowledge with citations.

"When would you fine-tune vs. use RAG?" RAG for knowledge gaps (current, org-specific info); fine-tune for format/vocabulary adherence on high-volume tasks. Try RAG first.

"What is the CDS Hooks 5-second SLA?" EHR will timeout the CDS service at 5 seconds; return empty cards {cards: []} on timeout, never a 500 error.

"What is PHI-safe logging?" Log metadata only: userid, patientid, action, model, token counts. Never log prompt text or response text.

"What is the minimum necessary standard?" Only include in AI context the PHI fields the use case actually requires. Drug interaction check: medications + allergies only, no name/address.

"What is a circuit breaker?" After N failures, stop sending requests to the failing provider and route to secondary. Reset after a cooldown period.

"What is semantic caching?" Cache query-response pairs; on new query, embed and compare to cached embeddings — return cached response if cosine similarity ≥ threshold.