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Artificial IntelligenceApril 14, 20249 min read

RAG Chunking Strategies: Optimizing Document Processing for Retrieval

Chunking strategy significantly impacts RAG system quality—size, overlap, and method all matter.

#rag#chunking#embeddings#retrieval

RAG systems split documents into chunks for embedding and retrieval. Chunk size, overlap, and splitting strategy directly impact retrieval quality. Poor chunking produces irrelevant or incomplete retrieved context.

Chunking Approaches

Fixed-size chunking splits by character or token count. Semantic chunking respects document structure. Recursive splitting balances size with coherence. Each approach suits different document types.

  • Start with 500-1000 tokens per chunk as baseline
  • Use 10-20% overlap preserving context at boundaries
  • Respect document structure—headers, paragraphs, sections
  • Consider semantic chunking for structured documents
  • Test retrieval quality with your actual queries

Optimization

Evaluate chunking with retrieval metrics on representative queries. Smaller chunks improve precision but may lose context. Larger chunks preserve context but reduce precision. Iterate based on your use case.

Tags

ragchunkingembeddingsretrievalnlp