Inside document chunking: why how you split text decides RAG accuracy

Before a retrieval-augmented AI can find anything, someone has to decide how to cut documents into searchable pieces. That decision — chunking — is one of the most under-discussed drivers of answer quality.

What a chunk actually is

A chunk is a passage of text, usually a few hundred tokens, that gets embedded and stored for search. When a user asks a question, the system compares the question against chunks and returns the closest matches. The model only sees what the chunker handed over.

Why naive splitting fails

Split a 40-page policy every 500 characters and you'll slice sentences mid-clause, separate a quote from its speaker, and strip headings from the paragraphs they govern. A retrieved chunk about "up to three days" becomes meaningless if the surrounding words "remote work" are in a different chunk.

Strategies that work in practice

• Structural chunking. Split on headings, sections, and list boundaries first. Documents are already organized — respect that structure.
• Semantic chunking. Group sentences by topic shift rather than fixed length. Longer when ideas flow together, shorter when they don't.
• Overlap. Repeat 10–20% of text between adjacent chunks so a sentence that straddles a boundary still gets caught.
• Attach context. Prepend the document title and nearest heading to every chunk. The retrieval system then sees "Remote Work Policy > Eligibility > ..." instead of a floating paragraph.

Special cases

Tables, code, and legal clauses need their own handling. A pipe-split table loses meaning row by row — keep it whole or render it as rows with headers repeated. Legal clauses should stay atomic so a definition never separates from the term it defines.

How to test

Build a question-to-passage test set. Re-chunk, re-index, and measure retrieval recall. If recall jumps, your chunker was the bottleneck — not the model.

For the broader grounding picture, see how to reduce AI hallucinations in enterprise answers.