IVQ 351-400

Section 36: Prompt Engineering & Templating (10 Questions)

  1. What are the key components of an effective prompt?

  2. How do you use few-shot examples inside prompts effectively?

  3. What is a system prompt, and how does it differ from user input?

  4. How do you use conditional logic within templated prompts?

  5. What is the role of stop sequences in prompt design?

  6. How do you reduce verbosity in GenAI responses?

  7. How would you convert user feedback into improved prompt versions?

  8. How do you chain multiple prompts in a multi-step task?

  9. What is prompt injection and how do you defend against it at the prompt level?

  10. How do you A/B test prompt variants in production?

Section 37: Interactive Apps & UI Integration (10 Questions)

  1. How would you build a live chat GenAI assistant with FastAPI and WebSockets?

  2. How do you stream GenAI responses in real-time to the front end?

  3. What’s the best UI pattern for displaying GenAI citations or sources?

  4. How do you implement “undo” or “edit and retry” in a GenAI app?

  5. How do you support multi-turn dialogue in a web-based GenAI tool?

  6. What are common accessibility issues in GenAI-powered interfaces?

  7. How do you design fallback or human takeover in a GenAI UX?

  8. What’s the role of tokens used vs. characters shown in pricing dashboards?

  9. How do you implement customizable personas for users in GenAI UIs?

  10. How can voice input/output be integrated into GenAI systems?

Section 38: Symbolic & Hybrid Reasoning (10 Questions)

  1. What is neuro-symbolic AI and how does it relate to LLMs?

  2. How can symbolic rules improve GenAI’s factual accuracy?

  3. How do you combine structured knowledge graphs with LLMs?

  4. What are the tradeoffs between symbolic systems and LLM-only pipelines?

  5. How do you create explainable reasoning traces using LLMs?

  6. What’s the role of constraint satisfaction in hybrid reasoning?

  7. How can you augment LLMs with logic programming tools like Prolog?

  8. What’s the role of semantic parsers in bridging LLMs and structured data?

  9. How do hybrid systems outperform LLMs in complex QA tasks?

  10. What are emerging frameworks for combining LLMs and rules-based systems?

Section 39: Performance Optimization & Latency (10 Questions)

  1. What are the key factors affecting LLM inference latency?

  2. How do you optimize prompt + retrieval workflows for low latency?

  3. What is speculative decoding and how does it reduce response time?

  4. How do you pre-warm models in production to reduce cold start times?

  5. What is KV cache reuse, and how does it help with streaming inference?

  6. How do you decide between batching vs. concurrency in API use?

  7. What is the cost vs. performance tradeoff when using GPT-3.5 vs GPT-4 vs Claude?

  8. How do you use token count estimators to reduce unnecessary calls?

  9. What are options for deploying LLMs with GPU autoscaling?

  10. How do you architect latency-sensitive GenAI experiences?

Section 40: Observability, Analytics & Monitoring (10 Questions)

  1. How do you log user queries and model responses safely?

  2. What are key metrics to track in GenAI product telemetry?

  3. How do you identify prompt drift in production?

  4. How do you measure output consistency across versions?

  5. What’s the role of LangFuse or PromptLayer in prompt monitoring?

  6. How do you visualize cost per feature or per user in GenAI APIs?

  7. What tools help you analyze token usage and model ROI?

  8. How do you monitor hallucination frequency in GenAI logs?

  9. What dashboards would you show to product teams vs. engineering teams?

  10. How do you alert on API spikes, model failures, or unsafe output?

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