RAG Fundamentals Interview Questions

Q1 What is Retrieval-Augmented Generation (RAG)? ✓ Answered → Q2 Explain the architecture of a basic Naive RAG system. ✓ Answered → Q3 What are the main components of a RAG pipeline? ✓ Answered → Q4 What role do embeddings play in RAG systems? ✓ Answered → Q5 What is a vector database and why is it used in RAG? ✓ Answered → Q6 What are the main challenges when building RAG systems? ✓ Answered → Q7 How do you evaluate the performance of a RAG system? ✓ Answered → Q8 What are common hallucination problems in RAG systems? ✓ Answered → Q9 How can caching improve RAG performance? ✓ Answered → Q10 What are the differences between RAG and fine-tuning? ✓ Answered → Q11 How do you approach feature engineering for NLP tasks, and can you give an example from your experience? Generate → Q12 Can you walk me through your process for building a GenAI tool using LangChain and OpenAI APIs? Generate → Q13 Can you walk me through your process for developing and deploying a machine learning model, and can you describe your experience with AWS? Generate → Q14 Can you describe your experience with streamlit and how you've used it to deploy machine learning models? Generate → Q15 Can you explain the concept of continuous learning and how you've applied it in your projects? Generate → Q16 Can you walk me through your process for building a CNN-based model, and can you describe your experience with PyTorch? Generate → Q17 Can you explain the concept of analytical thinking and how you've applied it in your projects? Generate → Q18 Can you describe your experience with Jupyter Notebook and how you've used it to develop and deploy machine learning models? Generate → Q19 Can you explain the concept of team collaboration and how you've applied it in your projects? Generate →