Customer Discovery and Use‑Case Scoping
This area matters because selecting the right problems—and scoping them well—determines downstream success. Interviewers evaluate how you qualify opportunities, surface constraints, and define measurable outcomes. Strong performance shows a structured discovery process, clear success metrics, and an ability to align mixed stakeholders (business, product, legal, security).
Be ready to go over:
- Stakeholder mapping – Identifying economic buyers, users, approvers, and potential blockers.
- Problem framing and ROI – Turning open‑ended requests into quantified hypotheses with KPIs.
- Pilot design – Crafting a minimal, measurable pilot with explicit acceptance criteria.
- Advanced concepts (less common) – Regulated‑industry scoping, data residency constraints, content moderation workflows, multi‑BU rollouts.
Example questions or scenarios:
- “Walk me through how you qualified and scoped an LLM pilot that became a production deployment. What metrics did you set?”
- “A customer asks for ‘ChatGPT for all employees.’ How do you structure discovery to find a focused, high‑ROI first use case?”
- “Describe a time legal or security concerns threatened a deployment. How did you adjust scope and expectations?”
LLM Application Architecture and Integration
This assesses your ability to design robust systems using the OpenAI API and common enterprise components. Interviewers want to see your command of request flows, context management, retrieval patterns, latency/cost controls, and resilience. Strong candidates articulate options, limits, and trade‑offs grounded in real constraints.
Be ready to go over:
- Request orchestration – Tool/function calling, multi‑turn state, streaming, and retries.
- RAG patterns – Indexing strategy, chunking, embeddings, and retrieval quality trade‑offs.
- Performance and cost – Token budgeting, caching, batching, throughput planning, fallback strategies.
- Advanced concepts (less common) – Multi‑tenant isolation, zero‑data retention paths, red/black testing, multi‑model routing.
Example questions or scenarios:
- “Design a RAG system for a 50k‑document internal knowledge base with strict latency targets.”
- “How would you structure retries, backoff, and fallbacks to handle transient API errors at scale?”
- “A customer’s API spend spiked 80% month‑over‑month. Diagnose likely causes and propose mitigations.”
Prompting, Evaluation, and Quality Assurance
LLM solutions live or die by reliable outputs. Interviewers test your approach to prompt design, offline/online evaluation, and mitigation of hallucinations. Strong answers show a repeatable evaluation framework, clear test sets, and governance for updates without regressions.
Be ready to go over:
- Prompt patterns – Instructions, structured outputs, few‑shot examples, and tool selection.
- Automated evals – Golden sets, rubric‑based scoring, semantic similarity, error taxonomies.
- Guardrails – Hallucination reduction, refusal handling, output validation.
- Advanced concepts (less common) – Domain‑specific rubrics, human‑in‑the‑loop review, canary deployments with eval gating.
Example questions or scenarios:
- “How do you build an evaluation harness to compare two prompt versions for a classification task?”
- “Describe techniques to reduce hallucinations in a customer‑facing assistant interacting with proprietary data.”
- “What metrics would you monitor post‑launch, and how would you use them to drive prompt updates?”
Security, Privacy, and Safety
Enterprise adoption hinges on trust. Interviewers evaluate your fluency with data handling, privacy, and responsible use. Strong performance includes explaining privacy options, scoping data flows, and planning safety reviews and monitoring.
Be ready to go over:
- Data flow and governance – What data is sent, retained, redacted, or encrypted.
- Access and controls – Authentication, authorization, secrets management, auditability.
- Safety posture – Policy enforcement, abuse prevention, incident response paths.
- Advanced concepts (less common) – PII/PHI handling, content review pipelines, region‑based routing.
Example questions or scenarios:
- “A healthcare customer wants to process clinical notes. What questions and safeguards do you put in place?”
- “Walk through a data‑flow diagram for a customer support assistant and highlight privacy controls.”
- “How would you respond to a customer escalation about a potentially unsafe model output?”
Executive Communication and Influence
You will often present to senior stakeholders and reconcile diverse priorities. Interviewers assess clarity, brevity, and the ability to connect technical detail to business outcomes. Strong candidates have crisp narratives, anticipate objections, and close with next steps.
Be ready to go over:
- Narrative structure – Situation, approach, results, learnings; tie to ROI and risk reduction.
- Objection handling – Cost, safety, change management, vendor lock‑in.
- Enablement – Hand‑offs, documentation, and upskilling plans.
- Advanced concepts (less common) – Multi‑quarter adoption roadmaps, value realization plans.
Example questions or scenarios:
- “Give a 5‑minute executive overview of a successful AI deployment you led. What mattered to the CFO vs. CISO?”
- “How do you handle a skeptical engineering leader who doubts LLM reliability?”
- “Present a 30‑60‑90 day rollout plan for a sales‑assist pilot.”
