How Do You Make RAG Faster?
To make a RAG system faster, focus on the retrieval layer, where most latency accumulates. The main levers are reducing the number of vectors searched, using efficient indexing, minimizing query-time reranking, caching frequent queries, and choosing faster embedding models. Reducing the size of the vector index is often the highest-leverage change, because query latency grows with the number of vectors scanned per query.
Where RAG latency comes from
RAG latency accumulates across query embedding, vector search, optional reranking, and language model generation. The vector search stage scales with the number of vectors in the index, so a larger index means slower queries, and reranking adds further latency on every query.
The two highest-leverage changes
First, reduce index size, since search latency grows with the number of vectors scanned. Second, remove unnecessary query-time stages. If a clean index makes reranking optional, dropping that stage removes its per-query latency entirely. Green Vectors reduces index size and improves first-pass relevance, addressing both at once.
More questions
Green Vectors reduced vector count by up to 99.5% and improved query latency by up to 4x at 15-million-vector scale.