The claim that needs examining

A common framing in RAG tutorials goes like this: fixed-size splitting is a naive baseline, semantic chunking is the smarter upgrade, and agent-based (propositional) chunking is the frontier. Each step sounds like a clean improvement. The reality in production retrieval pipelines is more complicated, and the "upgrade" path often introduces failure modes that are harder to debug than the ones it replaces.

This post walks through the four main chunking families, where each one actually breaks, and what that means for choosing a strategy on a real corpus.

Four chunking strategies and their real tradeoffs

Rule-based splitting

This covers fixed-character splitting, fixed-token splitting (using the same tokenizer as the target model), content-aware splitting with tools like spaCy or NLTK, recursive splitting by structural hierarchy (sections, then paragraphs, then sentences), and format-specific splitting for Markdown, LaTeX, or HTML.

The standard criticism is that rule-based…