When a RAG system gives a wrong answer, the model is usually blamed. In practice the failure often happens earlier: the right passage never reached the model, because it was split in the wrong place, scored badly or ranked too low.

In 2026 there is no shortage of advice about how to fix this. Semantic chunking, late chunking, contextual retrieval, proposition chunking, agentic search, million-token context windows. Most of it comes from vendors or tutorials. This year, though, several large independent studies tested these ideas head to head, at scale, with proper statistics.

This article pulls that evidence together.…