Text Chunking for RAG: Strategies, Examples, and Evaluation

Text chunking for retrieval systems should balance contextual completeness with precise retrieval, using boundaries that preserve the specific evidence users need rather than applying a universal size or overlap setting. Strategies include fixed-size, recursive, structure-aware, semantic, and parent-child chunking, each with different tradeoffs involving source fidelity, implementation complexity, and context budgets. Reliable extraction is essential because chunking cannot restore lost document structure, particularly in PDFs, tables, slides, and code; source identifiers, headers, versions, permissions, and locators should remain attached to chunks. A simple word-based splitter can provide a reproducible baseline, but production systems should use model-aware token limits and preserve source-native citation spans. Overlap should be tested against realistic boundary failures because it can preserve conditions split across chunks but may also create duplicates that displace useful evidence. Evaluation should use fixed questions and supporting passages to measure evidence retrieval, completeness, duplication, answer correctness, cost, latency, and indexed volume under comparable retrieval budgets. Structure-aware splitting is generally a useful starting point when formatting is reliable, while semantic boundaries and parent-child retrieval may help particular collections, but chunking must be evaluated alongside filtering, source freshness, retrieval, and answer generation across the full RAG pipeline.

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