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All System Design AI Solutions Architecture & AI Engineering Interview Questions (61)

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Part 25 of 28

Vector Search and Production RAG: ANN Indexes, Multi-Tenancy, Freshness, and Evaluation

How approximate nearest neighbour indexes (HNSW, IVF, product quantisation) work and trade recall for speed, then the production concerns the basic RAG guide skips: permission-aware retrieval, multi-tenancy, real-time updates, hybrid search, and RAG evaluation.

Created 2026-09-29 · Updated 2026-10-15 · AI Solutions Architecture & AI Engineering

Multi-Tenant RAG: Retrieving the Right Answer from the Right Customer

How to stop one company's documents leaking into another company's answers: tenant isolation at ingestion, retrieval, context assembly and caching, and how to choose between a shared index, namespaces and dedicated indexes.

Created 2026-10-04 · Updated 2026-10-15 · AI Solutions Architecture & AI Engineering

RAG System Design: How to Ground an AI's Answers in Your Own Data

Why a fluent AI answer can still be wrong — chunking, embeddings, hybrid search and reranking, and when retrieval beats fine-tuning or a bigger context window.

Created 2026-09-18 · Updated 2026-10-15 · AI Solutions Architecture & AI Engineering