IVQ 701-750

Section 71: GenAI Orchestration & Runtime Systems (10 Questions)

  1. What are the benefits of using LangGraph for structured agent workflows?

  2. How does an LLM orchestrator differ from a general-purpose workflow engine like Airflow?

  3. How would you design a state machine for a GenAI multi-turn process?

  4. What are the advantages of memory-aware orchestration in GenAI systems?

  5. How do you handle rollback or cancellation in multi-step GenAI agents?

  6. How can you monitor execution traces across tools, prompts, and user sessions?

  7. What are failure recovery patterns in orchestration of LLMs and APIs?

  8. How does orchestration change when working with streaming vs. completion-based models?

  9. How would you structure a microservices architecture for GenAI agents?

  10. What tools support tracing across LangChain, FastAPI, and Qdrant in a full-stack GenAI app?

Section 72: Model Compression, Distillation & Quantization (10 Questions)

  1. What is model distillation, and how does it reduce inference latency?

  2. How does quantization affect attention patterns and token alignment in transformers?

  3. What are best practices for distilling a code generation model for production?

  4. How do LoRA and QLoRA compare in terms of performance and cost?

  5. What are common accuracy tradeoffs in INT4 vs INT8 quantized LLMs?

  6. How do you evaluate performance post-distillation (BLEU, BERTScore, etc.)?

  7. What’s the role of PEFT in task-specific fine-tuning for small devices?

  8. How can pruning and sparsity be applied to generative architectures?

  9. What are common pitfalls when using quantized models with retrieval-based systems?

  10. How would you chain multiple lightweight models to act like a heavier LLM?

Section 73: ROI & Economics of LLMs (10 Questions)

  1. How do you estimate cost-per-call in a GenAI API deployment at scale?

  2. What’s the break-even point for self-hosted LLMs vs. OpenAI pricing?

  3. How do you model ROI for a GenAI feature embedded in a SaaS product?

  4. What pricing strategies work best for AI-enhanced product tiers?

  5. How do caching strategies impact monthly token costs?

  6. What’s your approach to cost forecasting across multiple GenAI endpoints?

  7. How would you track cost per feature request in a multi-LLM system?

  8. How do you measure ROI when GenAI replaces manual content generation?

  9. What role does latency cost (in user wait time) play in pricing tiers?

  10. How would you design cost-aware prompt routing using multiple model backends?

Section 74: AI Ethics, Bias & Governance in Action (10 Questions)

  1. How do you audit for demographic bias in summarization or translation models?

  2. What are your escalation protocols for AI-generated harmful content?

  3. How do you align product and legal teams around responsible GenAI usage?

  4. What’s the role of third-party model audits in enterprise AI governance?

  5. How do you apply fairness metrics to prompt-level evaluation?

  6. What organizational safeguards are needed to prevent GenAI misuse?

  7. How do you conduct bias and fairness reviews of prompts used by customer-facing agents?

  8. What’s your view on disclosing AI-generated content to end-users — opt-in, opt-out, or visible by default?

  9. How would you handle a public GenAI output incident (e.g., offensive, misleading)?

  10. What are your practices for documenting known limitations of GenAI features?

Section 75: Generative UX Patterns & Product Thinking (10 Questions)

  1. How do you design interfaces that balance GenAI autonomy with user control?

  2. What is progressive disclosure in GenAI UX and when should you use it?

  3. How do you present AI confidence or uncertainty in responses?

  4. What are best practices for revising, rerunning, or refining GenAI answers?

  5. How do you segment GenAI experiences for different user personas (e.g., novice vs. expert)?

  6. What’s your approach to onboarding users into a GenAI tool?

  7. How do you visualize long GenAI outputs in a scrollable or collapsible way?

  8. What are the design tradeoffs between chat-based vs. form-based GenAI inputs?

  9. How do you build trust in GenAI for critical tasks (e.g., finance, legal)?

  10. How do you collect product telemetry to improve GenAI UX over time?

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