Your question is Retrieval-Augmented Generation (RAG) Overview. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Explain how Retrieval-Augmented Generation (RAG) works.
Asked in the technical round stage. GenAI discussion portion.
Describe the end-to-end flow from user query through document chunking, embedding, indexing, retrieval, optional ranking, prompt construction, generation, and response validation. Explain why RAG is used instead of relying only on model parameters or fine-tuning. Keep the answer practical and address evaluation, monitoring, freshness, grounding, and common failure modes.
