Chunking may matter more than embeddings in a RAG pipeline
victorialslocum · x · 2026-07-28
Chunking is the most underrated decision in a RAG pipeline.
The post argues that document splitting often matters more than the choice of vector database or embedding model, because it determines what the agent can actually retrieve and how much useful context fits into the model window.
It contrasts two common approaches:
- Fixed-size chunking: a simple token-based split with overlap, but blind to document structure.
- Recursive chunking: a more structure-aware method that preserves meaning better while still controlling chunk size.
The core tradeoff is precision vs. context: chunks must be small enough to retrieve accurately, but large enough to remain useful once surfaced to the model.
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