Design a RAG system end to end
AmazonApplied Scientist · Technical Screen
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This question evaluates a candidate's ability to design a production-grade Retrieval‑Augmented Generation (RAG) system, testing competencies in scalable ML system architecture, embedding and vector retrieval strategies, prompt orchestration, freshness and latency engineering, security/access controls, and evaluation metrics.
Design a retrieval-augmented generation system for enterprise text. Specify the ingestion pipeline (chunking, embedding generation, indexing), retrieval strategy (vector search, hybrid retrieval, reranking), prompt orchestration, grounding and citations, freshness handling, latency/throughput targets, and privacy controls. Discuss evaluation for relevance and answer quality, approaches to reduce hallucinations, and how you would scale and monitor the system in production.
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