Chunking Strategies
Build the correct enterprise document processing pipeline
“A legal tech startup built a RAG system over 50K contracts. Retrieval accuracy was 34%. The problem wasn't the embedding model or vector DB — it was their chunking strategy: fixed 1000-character chunks that split sentences mid-thought and stripped all metadata. Re-chunking with RecursiveCharacterTextSplitter at 512 tokens pushed accuracy to 69% overnight.
Chunking Strategies Comparison
Why Chunking Matters More Than Your Embedding Model
The biggest surprise in production RAG: chunk quality matters more than embedding model choice. The FloTorch 2026 benchmark proved it — recursive character splitting at 512 tokens achieved 69% retrieval accuracy, while semantic chunking managed only 54%.
Why does semantic chunking underperform? On dense technical prose, semantic boundaries are ambiguous. The splitter creates tiny fragments (50-100 tokens) that lose context, or huge chunks that dilute relevance.
- Parse before split: Raw HTML tags and PDF artifacts corrupt chunk content
- Split before tag: Metadata is attached per-chunk, not per-document
- Tag before store: Without metadata, you cannot filter at query time
- Overlap prevents lost context: 10-15% overlap keeps boundary sentences in both adjacent chunks
Parse format first → choose chunk size based on your query type (512 for point lookups, 128 for keyword-heavy, 1024 for summarization) → always add overlap → always add metadata. Semantic chunking adds 100-300ms per document and underperforms recursive splitting on technical text. Reserve it for narrative prose like books or articles.
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Chunking Strategies
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