Designing retrieval systems around advanced chunking strategies

Chunking strategy directly determines retrieval quality in RAG systems: chunks must be small enough to keep embeddings semantically focused and fit context windows, yet large enough to preserve meaning and context. Databricks recommends choosing chunk size and overlap based on document structure, embedding model limits, and expected query type rather than a single default setting.

1 · Learn the must-know

• 1Fixed-size chunking is simple and fast but risks splitting sentences or ideas mid-thought, hurting embedding quality and retrieval relevance.
• 2Recursive or structure-aware chunking (splitting on paragraphs, headers, or sections before falling back to characters) generally preserves semantic coherence better than naive fixed-size splitting.
• 3Adding overlap between chunks helps retain context across chunk boundaries but increases index size and redundancy, so overlap should be tuned, not maximized blindly.
• 4Chunk size should respect the embedding model's max token/context limit; oversized chunks get truncated silently, causing information loss.
• 5Document type matters: code, tables, and structured data often need custom or format-aware chunking logic rather than generic text splitters.
• 6Evaluate chunking strategy empirically (via retrieval relevance metrics) rather than assuming one approach works uniformly across all document types in a corpus.

2 · Check your understanding