Choose RAG Chunking by Document Type and Answer Shape

Effective chunking should be selected according to the evidence required by different document types and user questions, since policies, procedures, tables, code, and meeting notes each face distinct boundary risks. A document-and-question matrix can identify the supporting span needed for representative queries before splitters are compared, preventing topic matches from masking missing conditions or context. Evaluations should keep corpus versions, retrieval settings, ranking, and answer prompts constant while measuring both fixed result counts and context-token budgets, distinguishing duplicate evidence from useful coverage. Chunking strategies should also be tested against document edits and deletions to assess reprocessing costs, citation stability, and whether outdated content can still affect answers. Products may use a simple default for prose while applying measured exceptions for structures such as tables and code, with each exception documented through representative failure cases; managed alternatives such as Supermemory can be assessed using the same question matrix and by reviewing the evidence returned.

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