How Do You Improve RAG Accuracy?
To improve RAG accuracy, improve both what is retrieved and how it is used. The main levers are better chunking that preserves context, reducing index noise from redundant vectors, hybrid search that combines keyword and vector retrieval, reranking to refine order, and higher-quality embedding models. Reducing redundant vectors is often overlooked, because a noisy index degrades relevance before any later step can help.
Fix the index first
Accuracy starts with retrieval. If the index is full of near-duplicate, redundant vectors, the search space is noisy and relevant results compete with redundant ones. Cleaning the index at ingestion and using context-preserving chunking improve what retrieval returns, which raises the ceiling for every later step.
Why this beats patching at query time
Hybrid search and reranking are query-time corrections that work on whatever the index returns. They help most when the index is already clean. Fixing accuracy at the source, by removing redundancy before storage, improves results for every query without adding per-query work.
More questions
Green Vectors improved search quality by up to 59% by eliminating redundant vectors.