IVQ 401-450
Section 41: LLM Comparison & Model Selection (10 Questions)
How do you compare proprietary LLMs like GPT-4, Claude, Gemini, and Mistral for a given use case?
What criteria would you use to choose between a 7B and a 70B model?
When is it better to use a distilled or quantized model instead of the full one?
How do you benchmark multiple LLMs for summarization vs. generation?
What is the importance of context window in model selection?
How do you factor in inference speed when selecting a GenAI model?
What are the trade-offs between open-source and proprietary LLMs?
How does finetuning affect model comparability?
How do you run a fair A/B/C test across different LLMs in a production setting?
What are the implications of model licensing (e.g., Apache 2.0 vs. non-commercial) in choosing LLMs?
Section 42: Safety Evaluations & Red Teaming (10 Questions)
What is the difference between adversarial prompts and jailbreak prompts?
How do you set up red teaming for a GenAI product before launch?
What are the most common failure modes of LLMs in production?
How do you test LLMs for bias, toxicity, and misinformation?
What datasets are used for LLM safety benchmarks (e.g., TruthfulQA, RealToxicityPrompts)?
How would you design a human-in-the-loop review system for GenAI output moderation?
How do you build internal reporting dashboards for misuse detection?
What are effective thresholds for blocking vs. warning in unsafe outputs?
How do you measure effectiveness of safety interventions over time?
What is “red teaming-as-a-service” and how can it help scale GenAI safety testing?
Section 43: Feedback Loops & Continuous Improvement (10 Questions)
How can you collect structured feedback on GenAI outputs?
What are the pros and cons of thumbs-up/down systems for LLMs?
How do you use user corrections to improve a prompt template?
How do you incorporate feedback into prompt routing logic?
What is reinforcement learning from human feedback (RLHF), and when would you use it post-deployment?
How do you prevent “feedback poisoning” in open feedback systems?
How do you distinguish between UX complaints and model behavior issues?
What are automated metrics that correlate with human preference?
How do you set up dashboards that track feedback over time by use case?
How can feedback be used to trigger re-training or model switching?
Section 44: Autonomous GenAI Workflows (10 Questions)
How do you design an agent that can plan, retrieve, decide, and execute across tools?
What is the difference between reactive, proactive, and autonomous agents?
How do you build guardrails around tool-using agents (e.g., search, email, calendar)?
What are signs that an autonomous agent is "looping" or stuck in reasoning?
What is a task decomposition agent and when should you use one?
How can you assign confidence scores to agent actions?
How would you evaluate autonomy vs. accuracy trade-offs?
What strategies can prevent prompt escalation or tool abuse by agents?
How can autonomous agents collaborate on shared memory or context?
How do you manage cost predictability in long-running GenAI agent workflows?
Section 45: Enterprise Adoption & Strategic Integration (10 Questions)
What are the top blockers to GenAI adoption in traditional enterprises?
How do you align GenAI strategy with business KPIs?
How do you work with legal teams on GenAI compliance reviews?
How do you address internal resistance to AI automation?
What’s your playbook for rolling out a GenAI capability across multiple departments?
How do you price GenAI-powered product tiers?
What are critical dependencies between GenAI features and data engineering teams?
How do you measure internal productivity lift from GenAI tooling?
What is your approach to evangelizing GenAI internally to non-technical stakeholders?
How would you assess whether a GenAI prototype is ready for production rollout?
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