IVQ 551-600
Section 56: Knowledge Grounding & Trustworthy Generation (10 Questions)
How do you ensure a GenAI model always grounds its output in source material?
What are techniques for reducing hallucinations in RAG workflows?
How do you tune a prompt to force citation or justification from a GenAI model?
What’s the difference between zero-shot grounding vs. retrieval-augmented grounding?
How do you evaluate whether an answer is grounded vs. inferred?
What’s the impact of chunk overlap on grounding quality?
How do you combine structured knowledge (SQL, graphs) with unstructured GenAI reasoning?
How can you improve source attribution in multi-source summarization?
How do you audit GenAI outputs for completeness and traceability?
What are techniques to dynamically rerank or filter ungrounded outputs?
Section 57: Model Explainability & Transparency (10 Questions)
How do you use attention weights to understand LLM behavior?
What is a saliency map, and how can it apply to text generation?
How do you generate a human-readable explanation of an LLM’s reasoning steps?
What are interpretable surrogate models, and when are they useful in GenAI?
What’s the role of “rationale generation” in explaining answers?
How do you trace prompt → response → decision in regulated systems?
How do you implement explainer modules in GenAI APIs for enterprise clients?
What’s the tradeoff between explainability and creativity in generation?
How do you identify attention collapse in large models?
What tools or libraries exist to visualize LLM decisions or token contributions?
Section 58: Global & Cross-Regional Trends (10 Questions)
How are Asian markets adapting GenAI differently than Western ones?
What are the implications of data sovereignty laws on LLM hosting in the EU?
How does multilingual LLM adoption vary in MENA or Africa?
What global GenAI innovation clusters are emerging beyond Silicon Valley?
How do regulations in China shape LLM capabilities vs. open models?
What’s the role of local government and policy in promoting GenAI in developing economies?
How does internet censorship affect GenAI performance across regions?
What are the localization challenges for GenAI products in South America?
How are public-private partnerships shaping national LLM initiatives (e.g., UAE, India)?
How do infrastructure disparities affect equitable GenAI access worldwide?
Section 59: Security & Attack Surface Hardening (10 Questions)
How do you prevent prompt leakage through indirect output channels?
What is a jailbreaking attack and how can prompt rewriting defend against it?
How do you restrict LLM responses to only approved tool calls?
How do you detect and handle prompt injection across web-based LLMs?
What are common attack vectors in multi-agent GenAI systems?
How do you use content filters to catch policy violations at runtime?
What’s the role of AI firewalls or LLM security gateways?
How do you apply least privilege access to GenAI infrastructure?
How do you monitor for embedding inversion or training data reconstruction attacks?
How can LLMs be safely sandboxed in multi-tenant SaaS environments?
Section 60: GenAI-Driven Business Transformation (10 Questions)
How do you identify areas within a business that can be augmented with GenAI?
What does GenAI-driven cost reduction look like in operations or support?
How do you structure a GenAI center of excellence inside an enterprise?
What change management processes are needed for successful GenAI adoption?
How would you design an internal LLM to assist product managers with feature analysis?
How do you quantify the productivity gain from GenAI-based automation?
What’s your framework for running a GenAI pilot project within a business unit?
How do you integrate GenAI with legacy ERP or CRM systems?
What’s the difference between enabling GenAI as a feature vs. a product strategy?
How do you build internal trust and compliance when launching GenAI-powered systems?
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