Chunking: the most underrated decision in your RAG pipeline

Ask a team how their RAG pipeline works and they will tell you about the embedding model, the vector database, and maybe the reranker. Ask them how they chunk their documents and you will usually get "uh, 500 tokens with some overlap? Whatever the default was."

That default is quietly deciding the quality of every answer the system gives. Chunking is the highest-leverage, least-discussed decision in a RAG pipeline, and I want to convince you of that with concrete examples rather than hand-waving.

The refund policy that got sliced mid-sentence

Say your docs contain this refund policy:

## Refund policy

Chunking: the most underrated decision in your RAG pipeline

Related reading

Best Chunking Strategies for RAG Pipelines

RAG Pipeline Chunking Strategies: Split Documents for Better Retrieval

7 Chunking Strategies That Decide Whether Your RAG Works -…

Choosing the Right RAG Strategy A Complete Decision Guide to Chunking, Agentic…

Dynamic chunking for RAG: building context infrastructure that adapts

What I learned building a document chunking and embedding API for RAG