Chunk Segmentation Test

Concept

Semantic chunking divides documents into meaningful segments that can be independently quoted by LLMs. Each chunk should contain 100-150 words and represent a coherent concept. This structure helps AI systems extract precise citations rather than vague references to entire documents.

Implementation Steps

  1. Define clear boundaries - Use headings to mark topic transitions
  2. Provide complete context - Each section should be self-contained
  3. Use structured elements - Lists, tables, and examples improve citability

Evidence Table

Unit Characteristic Citation Benefit
Chunk Independently quotable High precision
Section Topically coherent Contextual clarity
Paragraph Single concept Exact attribution

SSG-CHUNKS-MARKER-2024: "Structured semantic chunking with hierarchical headings and tabular data facilitates precise LLM citation extraction for Answer Engine Optimization strategies."

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