Your question is Embedding and Vector Search Latency. 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).
What strategies do you use for optimizing embeddings and vector search latency when scaling across tens of millions of high-dimensional financial records?
Discuss the choices you would make across embedding generation, dimensionality reduction, indexing, sharding, filtering, batching, caching, and approximate nearest-neighbor search. Explain how you would benchmark recall, p95 and p99 latency, throughput, operational cost, and freshness, while preserving retrieval quality for downstream RAG. Include how you would detect regressions and protect sensitive financial data.
