Beyond Embedding Models: Why Chunking Is RAG's True Bottleneck

While RAG practitioners often chase exotic embedding models and rerankers, the real bottleneck lies in how documents are split into chunks—a choice that directly shapes what embeddings can work with. Recent research reveals that sophisticated semantic chunking rarely outperforms simple strategies; the winning advantage comes from augmenting chunks with surrounding context rather than algorithmic complexity. Understanding these fundamentals lets developers optimize RAG systems with data-driven strategies instead of trial-and-error.