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Chunking decides which pieces of a document become retrieval candidates. A passage that is too small may lose its meaning; a very large one may bring unrelated text into the answer context. There is no single chunk size that is best for every collection.
Chunk boundaries are one part of retrieval quality. Compare them alongside hybrid search and reranking, using a fixed evaluation set so you can explain which change helped.
Start with the documents, not a magic number
Inspect headings, tables, lists and repeated navigation text. A product specification and a legal-style policy do not necessarily benefit from the same boundaries. Keep metadata identifying the document, section and version so retrieved material can be traced.
Build a small set of questions with known supporting passages. Include an exact identifier, a question requiring neighbouring paragraphs and one whose answer is absent.
Run an ablation
Compare a baseline split with a structure-aware alternative. Then change overlap separately. Record retrieval relevance, whether evidence survives at boundaries, context length, duplicate passages and final answer support.
Avoid changing the embedding model, chunking method and generation prompt in the same comparison. You will lose the ability to explain which component helped.
Watch the maintenance cost
Overlap duplicates content. Updated documents may require replacing many chunks, and stale versions can remain discoverable unless deletion and version rules are explicit. Test a revised document as carefully as the initial import.
Discussion: Which document structure caused the most difficulty in your retrieval pipeline, and how did you test the fix?
Further reading: Hugging Face RAG evaluation.
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