Your question is Embeddings and Vector Search. Take a moment with it on the right.
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What are the key considerations when implementing embeddings and vector search for large-scale document retrieval?
Discuss the design choices from document ingestion and chunking through embedding generation, indexing, retrieval, reranking, and downstream RAG generation. Address scalability, metadata filtering, freshness, latency, cost, relevance evaluation, hallucination control, and prompt injection risks. Include concrete tradeoffs and a practical production architecture.