HNSW vs IVF, when do you pick each for a production vector index?

Why is cosine similarity preferred over Euclidean distance for text embeddings?

Name two situations where Flat brute force search is still the correct production choice in a 2026 vector pipeline.

Spot the conceptual error: 'We're using PQ instead of HNSW for our vector index because PQ is faster.'

Bi-encoders vs cross-encoders, where does attention cross the query and document?