IVQ 51-100
Section 6: Model Internals & Architecture (10 Questions)
What is layer normalization and why is it important in Transformers?
How does beam search differ from greedy decoding and top-k sampling?
What is temperature in GenAI models, and how does it affect output?
What is causal masking and where is it used?
Explain attention heads. Why use multiple heads?
How do you prevent exposure bias during training?
What is the difference between training and inference time masking?
What are the trade-offs between depth and width in transformer models?
Why do larger models sometimes perform worse than smaller fine-tuned ones?
How does dropout help prevent overfitting in GenAI models?
Section 7: Training & Optimization (10 Questions)
What is gradient checkpointing and why is it used in training large LLMs?
How would you train a model like GPT-2 on a custom dataset?
Explain the concept of data curriculum in model training.
What are some techniques to reduce hallucination during training?
How do you select hyperparameters for a GenAI model?
What role does batch size play in model convergence?
What is zero-shot vs. few-shot vs. fine-tuning?
Describe how mixed precision training works.
What is weight decay and how does it affect large model training?
How can transfer learning benefit GenAI development?
Section 8: Evaluation & Metrics (10 Questions)
How do you evaluate creativity in GenAI outputs?
What is BLEU score and when is it used?
Compare BLEU, ROUGE, METEOR, and BERTScore.
What is perplexity, and what does it tell you about a language model?
How do you evaluate factual correctness in LLM-generated answers?
What are human-in-the-loop evaluation methods?
How do you A/B test different prompts or models in production?
What’s the importance of response diversity in GenAI?
What is the Turing Test, and how does it apply to modern LLMs?
What are the limitations of automated evaluation metrics in GenAI?
Section 9: Real-World Scenarios & Systems Design (10 Questions)
Design a GenAI system that generates marketing content at scale.
How would you build a secure GenAI-powered HR assistant?
What architecture would you use for a GenAI-powered code assistant?
How do you add memory or context persistence to GenAI agents?
What’s the role of a vector store in a GenAI-powered search app?
How do you handle GenAI latency and scale in a production API?
How do you version and monitor GenAI models in production?
How would you implement feedback loops for improving GenAI output?
What are the data pipelines needed for a custom GenAI chatbot?
How do you optimize cost while using OpenAI APIs at scale?
Section 10: Innovation & Future Directions (10 Questions)
What are token-efficient architectures and why do they matter?
How do mixture-of-experts (MoE) models help with GenAI scaling?
What is speculative decoding in LLMs?
How is diffusion being used for multimodal GenAI tasks?
What are some promising open-source GenAI projects today?
How can GenAI be used for real-time data analytics?
What is the role of synthetic data in GenAI pipeline bootstrapping?
How do emerging standards like OpenGPTs or Model Catalogs help teams?
What are some unexplored opportunities in GenAI for enterprise SaaS?
What research direction in GenAI excites you the most right now?
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