System Design in the RAG Era: Vector DBs, Embeddings & the New Round

The round that didn't exist 18 months agoRetrieval-Augmented Generation has gone from "cool research technique" to "the thing every ML team is building" in roughly eighteen months. If you're interviewing for any role that touches AI/ML, LLMs, or backend engineering at an AI company, you will get RAG questions — and they won't be theoretical. They'll be "have you actually built this" questions.The shift matters because classical system design and AI-era system design test fundamentally different things. The classical round rewards you for knowing when to reach for Redis. The RAG round rewards you for knowing when not to reach for a vector database at all.Classical system design vs RAG-era system designDimensionClassical round (2020-era)RAG-era round (2026)Canonical problemDesign Twitter / URL shortener / rate limiterDesign a customer-support RAG agent / doc-search copilotCore data structureHash map, B-tree, logVector index (HNSW, IVF), embeddingScaling leverSharding, caching, replicatio

System Design in the RAG Era: Vector DBs, Embeddings & the New Round

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