Vector databases power the retrieval layer in RAG workflows by storing document and query embeddings as high‑dimensional vectors. They enable fast similarity searches based on vector distances.| AIMultiple
Dense vector search is excellent at capturing semantic intent, but it often struggles with queries that demand high keyword accuracy. To quantify this gap, we benchmarked a standard dense-only retriever against a hybrid RAG system that incorporates SPLADE sparse vectors.| AIMultiple
The effectiveness of any Retrieval-Augmented Generation (RAG) system depends on the precision of its retriever component.| AIMultiple
AI models sometimes generate data that seems plausible but is incorrect or misleading; known as AI hallucinations. According to Deloitte, 77% of businesses| AIMultiple