Naive RAG Interview Questions

Q1 What is Naive RAG and how does it work? Generate → Q2 What are the limitations of Naive RAG? Generate → Q3 Explain prompt augmentation in a RAG pipeline. Generate → Q4 What is Naive RAG and how does it work? Generate → Q5 What are the main components of a Naive RAG pipeline? Generate → Q6 How does document retrieval work in a Naive RAG system? Generate → Q7 What role do embeddings play in Naive RAG? Generate → Q8 What is a vector database and why is it used in Naive RAG? Generate → Q9 What is the difference between retrieval and generation in RAG? Generate → Q10 How are documents converted into embeddings in Naive RAG? Generate → Q11 What is chunking and why is it important in Naive RAG? Generate → Q12 What are the limitations of Naive RAG architecture? Generate → Q13 How does similarity search work in a vector database? Generate → Q14 What is cosine similarity and how is it used in RAG retrieval? Generate → Q15 What is the purpose of context injection in Naive RAG? Generate → Q16 How does a language model use retrieved documents to generate answers? Generate → Q17 What are some popular vector databases used for Naive RAG? Generate → Q18 How can you improve retrieval quality in a Naive RAG system? Generate → Q19 What are embeddings and how are they generated for text? Generate → Q20 What is the difference between keyword search and vector search? Generate →