RT Avi Chawla (@_avichawla)Researchers built a new RAG approach that:- does not need a vector DB.- does not embed data.- involves no chunking.- performs no similarity search.And it hit 98.7% accuracy on a financial benchmark (SOTA).Here's the core problem with RAG that this new approach solves:Traditional RAG chunks documents, embeds them into vectors, and retrieves based on semantic similarity.But similarity ≠ relevance.When you ask "What were the debt trends in 2023?", a vector search returns chunks that ...