IVQ 501-550
Section 51: Specialized Tooling & Ecosystem Knowledge (10 Questions)
What’s the role of LangSmith in prompt debugging and agent tracing?
How do you use Weights & Biases to monitor GenAI training experiments?
What’s the purpose of LlamaIndex in RAG systems, and how is it different from LangChain?
How do you use BentoML or MLflow for serving GenAI endpoints?
How do you build a sandboxed GenAI execution environment using Docker?
What are the pros/cons of Ollama vs. LM Studio for running LLMs locally?
What tools can track data lineage in GenAI pipelines?
How would you orchestrate multi-agent tasks using CrewAI or AutoGen?
What’s the benefit of vLLM over standard Hugging Face inference?
How can you integrate LangGraph into an existing RAG pipeline?
Section 52: Legal & Regulatory Navigation (10 Questions)
What are key GenAI-related provisions in the EU AI Act?
How does the concept of “high-risk AI” affect LLM use in healthcare or law?
How do you map GDPR rights (e.g., data erasure, portability) to GenAI logs and outputs?
What is the difference between model privacy and data privacy?
What regulatory reporting do you need for LLM misuse in financial applications?
What are the challenges in applying HIPAA compliance to LLM-powered tools?
How can a company prove model explainability to auditors or regulators?
What is “algorithmic impact assessment,” and how would you conduct one?
How do export controls apply to powerful LLMs like GPT-4 or Claude 3?
What does “right to explanation” mean in the context of GenAI?
Section 53: Custom Training, Tuning & Dataset Curation (10 Questions)
How do you decide between instruction tuning vs. RLHF vs. SFT?
What’s the ideal structure of a dataset for tuning on internal company knowledge?
How do you handle copyright risk when curating GenAI training data?
How do you balance quality vs. diversity in training corpus construction?
What is a tokenizer mismatch, and how does it affect fine-tuning?
What’s the process for converting chat transcripts into fine-tuning datasets?
How do you evaluate success when training domain-specific LLMs?
How would you train a small model to emulate tone/style of a specific brand?
How do you apply differential privacy to a fine-tuning process?
What open datasets are best suited for code generation fine-tuning?
Section 54: Next-Gen Human Interaction Models (10 Questions)
How do multi-turn memory systems differ from static context windows?
What’s the difference between “session memory” and “long-term memory” in chat agents?
How do GenAI systems simulate persona and consistency across sessions?
How would you implement emotion-aware response generation?
How do you detect boredom, confusion, or curiosity in a GenAI UX?
What’s the role of embeddings in powering smart suggestions mid-conversation?
How can you personalize LLM behavior using just metadata or interaction logs?
What are challenges in making agents respond empathetically and ethically?
How do you blend real-time speech recognition with LLM-powered dialogue?
What are best practices for tone adaptation in customer-facing GenAI?
Section 55: Automated GenAI Pipelines (10 Questions)
How would you design a nightly GenAI pipeline that indexes new PDFs into a vector DB?
What are best practices for chunking large documents for embedding?
How do you design a scheduler that decides what content to summarize or skip?
How do you track failed or partial generations in automated workflows?
How would you create a content moderation queue for GenAI output review?
How do you balance cost vs. freshness in automated RAG indexing jobs?
How can you use Prefect or Airflow to orchestrate GenAI + LLMops tasks?
What are good retry patterns for high-latency LLM calls?
How do you trigger retraining when new data changes semantic structure?
How do you set up monitoring for pipeline latency, vector quality, and model drift?
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