IVQ 251-300

Section 26: GenAI in Industry Use Cases (10 Questions)

  1. How can GenAI be applied in legal contract review automation?

  2. What are the risks of using GenAI in healthcare diagnostics?

  3. How do GenAI models support financial forecasting?

  4. What are the benefits of GenAI in e-commerce personalization?

  5. How can GenAI improve supply chain visibility?

  6. How do you use LLMs to enhance cybersecurity monitoring?

  7. How can GenAI assist in internal company knowledge search?

  8. What are GenAI’s implications for journalism and media generation?

  9. How is GenAI used in customer journey orchestration?

  10. How can GenAI automate compliance document generation?

Section 27: LLM Lifecycle Management (10 Questions)

  1. How do you manage model versioning in a production GenAI system?

  2. What tools do you use to monitor drift in GenAI model performance?

  3. How do you update and roll back prompt templates safely?

  4. What’s the role of A/B testing in GenAI prompt tuning?

  5. How do you manage dependency changes in LangChain or LlamaIndex apps?

  6. What is your CI/CD pipeline for GenAI model deployments?

  7. How do you manage prompt logs and traceability for audit purposes?

  8. How do you fine-tune vs. swap models in response to product needs?

  9. How do you handle sunset of outdated LLM versions in production?

  10. How do you make sure embedded vector data remains fresh over time?

Section 28: Cutting-Edge Research & Trends (10 Questions)

  1. What are SSMs (State Space Models) and how are they replacing Transformers?

  2. How does the RWKV architecture work?

  3. What are Retrieval-augmented Mixture of Experts (RMoE)?

  4. Explain the concept of toolformer models.

  5. How do “language agents with memory graphs” improve GenAI reasoning?

  6. What is the idea behind multi-agent collaborative LLMs?

  7. What is synthetic gradient and how does it speed up training?

  8. How is GenAI being applied in neuro-symbolic reasoning?

  9. What’s the role of instruction-following datasets in LLM performance?

  10. How are long-context models like Claude 3, Gemini 1.5 or LLaMA 3 changing interaction design?

Section 29: Human-AI Interaction & UX (10 Questions)

  1. How do you design user interfaces for GenAI assistants?

  2. What’s the role of uncertainty estimation in GenAI UX?

  3. How do you show citations and source confidence in RAG systems?

  4. How do you reduce cognitive load in GenAI UI outputs?

  5. How do you implement “Ask me anything” with guardrails?

  6. What are good ways to let users correct GenAI outputs?

  7. How can you measure UX friction in LLM-generated responses?

  8. How do you manage expectations around GenAI creativity vs. factuality?

  9. How do you provide “Explain this” interactions to build user trust?

  10. How would you handle fallback when LLM fails to answer?

Section 30: Governance, Privacy, and Policy (10 Questions)

  1. How do you enforce data retention limits in a GenAI workflow?

  2. What is differential privacy and how does it relate to LLMs?

  3. How do you redact sensitive data before feeding it into prompts?

  4. What are your steps for responding to a data subject access request (DSAR)?

  5. What are AI Bill of Rights principles and how do they affect GenAI?

  6. What are the top compliance standards relevant to GenAI deployment (e.g., HIPAA, SOC 2)?

  7. How do you perform third-party model risk assessments?

  8. What are model cards and why are they important in AI governance?

  9. What’s your incident response plan for GenAI misuse or harm?

  10. What’s the difference between model transparency and explainability?

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