Your question is High-Fidelity RAG for Proprietary Data. 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).
How would you design a RAG pipeline to ensure high-fidelity responses from a proprietary dataset?
Explain how you would ingest and chunk documents, select embeddings and retrieval methods, rerank results, construct the generation prompt, and enforce grounded answers with citations. Include offline and online evaluation for retrieval quality, answer faithfulness, refusal behavior, and prompt injection resilience. Discuss how you would balance quality, latency, and inference cost, and identify important failure modes.