Your question is Embeddings for Semantic Search at Scale. 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).
How would you build and serve embeddings for semantic search at scale, and how would you choose and index the vector store?
Explain the ingestion, embedding, indexing, retrieval, and serving design. Discuss model selection, freshness, filtering, sharding, replication, latency, cost, and how you would evaluate retrieval quality and production reliability.