GenAI interview guides / RAG System Design Interview Guide
RAG System Design Interview Guide
Design retrieval-augmented generation systems that are measurable, secure, low-latency, and robust to bad retrieval.
Start with the product contract
Define freshness, latency, citation, permissions, abstention, and failure costs before choosing embeddings or a vector database.
Retrieval pipeline
Separate ingestion, parsing, chunking, metadata, embedding, candidate retrieval, reranking, context assembly, generation, and citation validation.
Evaluation
Measure retrieval quality separately from answer quality. Include hard negatives, stale docs, permission-sensitive cases, and unanswerable questions.
Production trade-offs
Discuss chunking, top-k, reranking depth, caching, freshness, tenant isolation, fallbacks, latency, and cost.