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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.

10 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

RAG & Vector Databases

Q1. What is Dense Retrieval and how does it work?

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RAG & Vector Databases

Q2. What are the caveats of dense retrieval?

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RAG & Vector Databases

Q3. What are the best chunking strategies in RAG systems?

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RAG & Vector Databases

Q4. What is ANN and how is it useful in RAG systems?

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RAG & Vector Databases

Q5. What are the steps in Retrieval-Augmented Generation (RAG)?

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RAG & Vector Databases

Q6. What is query rewriting in RAG systems, and why is it important for effective retrieval?

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RAG & Vector Databases

Q7. What is Multi-query RAG?

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RAG & Vector Databases

Q8. What is query routing in RAG systems, and how does it improve retrieval?

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RAG & Vector Databases

Q9. What distinguishes multi-query RAG from multi-hop RAG?

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RAG & Vector Databases

Q10. What is Agentic RAG?

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