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Document chunking (for RAG)

AI concepts

Splitting source documents into retrieval-sized pieces with overlap and metadata so embeddings stay meaningful and answers cite the right span.

Bad chunking produces missed facts or poisoned context: chunks too large dilute relevance; too small split tables and sentences awkwardly. Teams tune size, overlap, headings, and metadata filters alongside hybrid search.

Engineering habit

Version your chunking with the index: when chunk rules change, re-embed or re-index deliberately.