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Technical implementation · AI Search Infrastructure
Definition
Semantic chunking splits content at natural topic boundaries detected by a model, rather than at a fixed character or token count. Each chunk contains a complete, coherent unit of meaning.
Why It Matters for AI Search
Produces tighter embeddings and better retrieval precision than fixed-size chunking because each chunk corresponds to a genuine topic boundary rather than an arbitrary position in the document. Not universally deployed in production pipelines — many systems still use fixed-size or heading-based chunking. Content structured with clear heading hierarchy and self-contained sections performs well under both strategies.
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