Faster Retrieval for Better, More Efficient RAG
Summary
Faster Retrieval for Better, More Efficient RAG discusses maximizing efficiency in retrieval-augmented generation (RAG) through improved maximum inner product search (MIPS). It introduces learned routing approaches (SupportNet and KeyNet) to replace centroid-based routing, enabling faster, more accurate retrieval on large embedding databases, with amortized training and integration with existing indices like FAISS IVF. The article highlights empirical results on latency and recall using Natural Questions, illustrating how learned retrieval can significantly improve performance in production ML systems.