The Hidden Architecture Behind Dense Vector Search (and Why It’s Hard to Scale)
Most people think dense vector search works like this: embed your documents store the vectors run cosine similarity Done. This is the biggest misunderstanding in modern AI systems. Dense vector search looks simple, but in real deployments it becomes one of the hardest layers to scale—and often the true bottleneck behind: slow RAG pipelines inconsistent…