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Deep Learning & Generative AI

Generative AI & Large Language Models (LLMs) Interview Questions

Interview questions on Generative AI & Large Language Models (LLMs) Interview Questions.

12 questions

All subtopics LLM Architectures & Foundations Tokenization & Embeddings Attention, KV Cache & Efficient Inference LLM Training & Pre-training Decoding Strategies & Prompt Engineering Fine-Tuning & Model Adaptation RAG & Vector Databases Alignment & Preference Optimization LLM Evaluation & Benchmarking Agents, Tool Use & Memory Multimodal Generative AI LLM Safety & Security LLMOps & Production

Fine-Tuning & Model Adaptation

Q1. What distinguishes LoRA from QLoRA in fine-tuning LLMs?

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Fine-Tuning & Model Adaptation

Q2. How can LLMs avoid catastrophic forgetting during fine-tuning?

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Fine-Tuning & Model Adaptation

Q3. How does PEFT mitigate catastrophic forgetting?

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Fine-Tuning & Model Adaptation

Q4. State the scenarios where you will use RAG over fine-tuning and vice versa?

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Fine-Tuning & Model Adaptation

Q5. What is the difference between parameter-efficient fine-tuning (PEFT) and full fine-tuning? Which is better?

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Fine-Tuning & Model Adaptation

Q6. What are adapters, and how do they enable parameter-efficient fine-tuning (PEFT)?

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Fine-Tuning & Model Adaptation

Q7. What is LoRA (Low-Rank Adaptation) and how does it enable parameter-efficient fine-tuning?

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Fine-Tuning & Model Adaptation

Q8. What is “intrinsic dimension” in the context of model fine-tuning, and why does it matter?

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Fine-Tuning & Model Adaptation

Q9. How does LoRA differ from adapters?

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Fine-Tuning & Model Adaptation

Q10. In LoRA fine-tuning, how are the low-rank matrices A and B initialized?

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Fine-Tuning & Model Adaptation

Q11. In LoRA fine-tuning, how is the rank chosen?

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Fine-Tuning & Model Adaptation

Q12. What is QLoRA, and how does it compare to standard LoRA?

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