What are Chunking Strategies? - Definition & Meaning
Learn what chunking strategies are, how to optimally split documents for RAG, and which methods fit your use case best.
Definition
Chunking strategies are methods to split long documents into smaller units (chunks) for embedding and retrieval. The choice of chunk size and strategy strongly determines retrieval quality in RAG systems.
Technical explanation
Methods: fixed size (e.g., 512 tokens with overlap), sentence-based (on sentence or paragraph boundaries), semantic (NLP to find logical units), recursive (hierarchical: paragraphs first, then sentences). Overlap prevents context loss at boundaries. Too small chunks lose context; too large increase noise and cost. Embedding models have max input length. For code: function- or class-based chunking.
How AVARC Solutions applies this
AVARC Solutions adapts chunking to the domain: for technical documentation we use semantic chunking; for legal text paragraph-based with overlap. We test retrieval quality with different strategies and sizes.
Practical examples
- A knowledge base with 256-token chunks and 50-token overlap for technical documentation.
- A legal RAG respecting chunk boundaries at paragraph level for coherent answers.
- A codebase search chunking per function so developers find targeted code snippets.
Related terms
Frequently asked questions
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