Best Practices for RAG Chunking

Chunking best practices are not universal constants; they are decisions tuned to your document type, your embedding model, and the questions your users ask. What follows is a repeatable process for making those decisions well, with the simulator as the measuring stick.

Start by reading the embedding model's documentation. Most embedding models are trained or fine-tuned on snippets within a specific character or token range, and chunk sizes far outside that range underperform regardless of how clean your text is. If the vendor recommends a token window, translate it to characters with the four-characters-per-token rule and set that as your upper bound.

Match chunk size to the length of real questions. If users ask short, targeted questions like "what is the refund policy?", smaller chunks win because the answer occupies a higher fraction of the retrieved text. If they ask long, multi-part questions, larger chunks that carry surrounding context win. Sample real queries first; the retriever is being asked to solve your queries, not a benchmark.

Prefer paragraph boundaries over raw offsets wherever the text is structured. A paragraph is a complete thought, and embedding a complete thought beats embedding a random slice of one. When a paragraph exceeds your cap, split it on sentence boundaries rather than mid-sentence, so each fragment remains grammatically self-contained.

Use overlap sparingly and deliberately. A small overlap — 10 to 25% — protects against phrases that straddle boundaries and costs little. Large overlaps inflate your vector index and every embedding call for marginal gain. If your questions frequently hit boundary text, prefer a slightly larger chunk size over a bigger overlap.

Attach metadata at chunk time. Source document, section heading, page number, and a document type field let the retriever filter and rerank, and they give the generator the citations it needs to be trustworthy. Chunking is the only stage where this information is still cheap to capture; decide the schema before you split.

Keep tabular and list content together. A table split mid-row or a numbered list split between items becomes meaningless in retrieval. For such structures, treat each row or each list item as its own semantic unit and include the header with it, so a retrieved fragment carries its own context.

Normalize the document before chunking. Strip inconsistent whitespace, collapse repeated newlines, and resolve encoding issues first, because every artifact you leave in becomes a phantom token and a subtle similarity-distance penalty. The simulator's paragraph splitting is only as clean as the text you feed it.

Validate with retrieved examples, not just with chunk counts. After choosing a strategy, take five representative questions, find the chunk each should match, and confirm the retriever actually surfaces it with useful neighbors. Chunk counts and token estimates are necessary checks, but retrieval quality is the acceptance test.

Document the decision. Record the chunker, size, overlap, and the benchmark that justified them in your repository. When the corpus or the embedding model changes, that record tells you exactly what to re-test. Chunking choices that look obvious in hindsight are opaque six months later without the notes.