IVQ 951-1,000

Section 96: Evaluation, Testing & Benchmarking (10 Questions)

  1. How do you design a test suite to evaluate GenAI response robustness?

  2. What are standard metrics for text summarization model evaluation?

  3. How would you test the factual consistency of a long-form output?

  4. What are challenges in evaluating GenAI creativity or novelty?

  5. How do you benchmark GenAI tools across different domains (e.g., legal vs. marketing)?

  6. What is a pass@k metric, and how is it used for code generation evaluation?

  7. How do you compare open-source and commercial LLMs objectively?

  8. What is the role of human judgment in GenAI evaluation pipelines?

  9. How do you test GenAI models for robustness to prompt rephrasing?

  10. What strategies help simulate real-world edge cases in model eval?

Section 97: Ethical Reasoning & Misinformation Defense (10 Questions)

  1. How can GenAI systems be misused to generate persuasive misinformation?

  2. What are technical approaches to flag potentially harmful or biased completions?

  3. How do you design GenAI systems to refuse unethical requests?

  4. What is narrative poisoning, and how can it affect GenAI training corpora?

  5. How do you balance freedom of expression with moderation in GenAI tools?

  6. How would you embed explainable disclaimers in GenAI outputs?

  7. What’s your view on watermarking LLM-generated content — useful or intrusive?

  8. How can LLMs support fact-checkers or content verifiers?

  9. What are the consequences of model hallucination in high-stakes environments?

  10. How can synthetic data contribute to de-biasing a generative model?

Section 98: Prompt Chaining, Flow Control & Composition (10 Questions)

  1. How do you chain prompts to execute sub-tasks with context continuity?

  2. What’s your approach to managing prompt length across long flows?

  3. How do you encode validation logic inside a multi-step LLM workflow?

  4. What are common bugs in complex prompt chaining implementations?

  5. How do you manage state between chained prompt components?

  6. How do you control output formats (e.g., JSON) in chained flows?

  7. What is prompt abstraction, and how does it improve scalability?

  8. How would you compose a summarizer → QA → feedback chain?

  9. How do you safely inject user-provided data into dynamic prompt chains?

  10. How do you build modular prompt functions reusable across flows?

Section 99: Vision, Roadmaps & Industry Impact (10 Questions)

  1. What are your predictions for the next major GenAI application paradigm shift?

  2. How do you envision GenAI transforming software engineering workflows?

  3. What emerging GenAI research directions excite you the most?

  4. How do you see the GenAI ecosystem evolving with open weights and decentralized models?

  5. What’s your vision for AI-native products — beyond adding AI features to existing tools?

  6. How do you plan for regulatory shifts when designing GenAI-powered systems?

  7. How should enterprises future-proof themselves for LLM evolution?

  8. What skills will be most valuable in a GenAI-native engineering team?

  9. How do you anticipate GenAI changing the nature of UI/UX design?

  10. How would you structure a GenAI innovation roadmap inside a mid-sized tech company?

Section 100: Human-AI Collaboration Patterns (10 Questions)

  1. What are best practices for human-in-the-loop GenAI systems?

  2. How do you design systems where humans correct or verify LLM decisions in real time?

  3. What are the challenges of handoff between AI-generated drafts and human reviewers?

  4. How do you design workflows for GenAI to support but not replace creative professionals?

  5. How do you signal uncertainty to human collaborators in AI-generated output?

  6. How do you enable structured feedback collection on GenAI behavior from users?

  7. What’s your strategy for combining human memory and GenAI memory in chat UX?

  8. How do you evaluate productivity gains from AI-human co-writing or co-design?

  9. What are examples of emergent collaboration patterns between agents and people?

  10. What does successful “co-pilot” design look like for AI-assisted work?

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