Because language models work within limited context and match meaning at the passage level, systems break content into chunks before indexing. When a query arrives, the retriever compares it against stored chunk embeddings and returns the closest matches. Answers are assembled from these fragments, so the quality of individual passages determines what gets used.
This mechanics has a clear implication: each section of a page should stand on its own. Acromatico structures content so natural chunks, such as a definition, a step list, or a comparison, are coherent in isolation. Well-formed chunks are retrieved more accurately and quoted more reliably by AI answer engines.
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Models have limited context windows and match relevance at the passage level. Splitting documents into chunks lets a retriever pinpoint the exact section that answers a query, feeding the model only the most relevant fragments rather than entire pages of mostly unrelated text.
Write sections that are coherent on their own, with clear headings and complete thoughts, so each becomes a strong standalone chunk. Avoid splitting a single idea across distant paragraphs, since fragmented meaning weakens how accurately a passage is retrieved and reused.
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