Semantic chunking is a text-processing technique used in retrieval-augmented generation (RAG) systems and AI search pipelines in which a document is divided into segments based on topical or semantic coherence rather than by arbitrary fixed character or token counts. A semantically chunked document groups sentences and paragraphs that address the same concept together, so that when a chunk is retrieved in response to a query, it contains complete, contextually coherent information rather than a fragment that starts or ends mid-thought. For SEO and AEO practitioners, semantic chunking is relevant because it influences which portions of a web page an AI search system retrieves and ultimately cites: content written in clearly delineated, self-contained sections covering one idea at a time is more likely to chunk well and be returned as a high-relevance passage. Heading structure, short paragraphs, and explicit topic sentences all support cleaner semantic chunk boundaries.