rag-dense-vs-sparse-vectors
IN premise — summaries/2026/08/24/wiki-Retrieval-augmented_generation-chunk-1.md
Created 2026-08-25T02:58:51+00:00
Dense vectors encode semantic meaning in compact form with few zeros; sparse vectors encode word identity as dictionary-length vectors that are mostly zeros.
Summary
Dense and sparse vectors are two fundamentally different ways to turn text into numbers: dense vectors are short and packed with meaning so that similar ideas land close together even if the wording differs, while sparse vectors are long and mostly empty, tracking exactly which dictionary words appear. This distinction matters because a system built on one type can match concepts it has never seen before, whereas the other can only recognize exact word overlap, so the choice shapes what the retrieval pipeline is actually capable of finding.