RAG Question Bank 2026: Retrieval‑Augmented Generation Practice Questions (Beginner to Intermediate)

A. RAG Fundamentals

1. What is Retrieval‑Augmented Generation (RAG) and how is it different from using a standalone LLM?
2. What are the two main components of a RAG system? Briefly explain the role of each.
3. Give three real‑world examples where RAG is a better choice than a plain LLM.
4. Why does RAG typically reduce hallucinations? Are there situations where it might still hallucinate?

B. Architecture & Design

1. Describe a simple high‑level architecture for a RAG‑based question‑answering system.
2. What are common choices for the retriever component (vector search, keyword search, hybrid)? When would you pick each?
3. How does chunking strategy (size, overlap, headings) affect RAG performance?
4. What is "prompt stuffing" in the context of RAG, and why can it hurt performance?

C. Failure Modes & Debugging

1. Your RAG system returns irrelevant passages even though the index seems correct. What could be going wrong in the retriever stage?
2. The retrieved documents are correct but the final answer is still wrong. What generator‑side issues could cause this?
3. How would you debug a case where RAG performs worse than a standalone LLM on a particular task?

D. Evaluation & Practical Considerations

1. How would you evaluate whether a RAG system is "good enough" for production use?
2. What is the difference between intrinsic and extrinsic evaluation for RAG?
3. When might you choose to fine‑tune a model instead of (or in addition to) using RAG?
4. How can human‑in‑the‑loop review make RAG systems safer for high‑risk domains (law, medicine, finance)?

How to Practice with This RAG Question Bank

Pick any RAG tutorial or your own mini project and try to answer these questions with concrete examples from your code. This combination of theory + your own implementation is what interviewers look for.