Essential techniques for splitting documents into optimal-sized chunks for Retrieval-Augmented Generation, including fixed-size, recursive, semantic, and document-based chunking with overlap strategies to preserve context.
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Chunking is the process of breaking down large documents into smaller, manageable pieces for vector embedding and retrieval. The right chunking strategy directly impacts retrieval accuracy, context preservation, and overall RAG system performance.
Description: Split text with a specific chunk size and optional overlap
Characteristics:
Recommended Sizes: 200-500 tokens with 10-20% overlap
Description: Iterate through separators until achieving preferred chunk size
Process:
Description: Group sentences based on semantic similarity of embeddings
Advantages:
Disadvantages:
Description: Split based on document structure
Methods:
Overlapping chunks mitigate context loss at boundaries:
Sliding Window: Create chunks that share overlapping portions of text
| Use Case | Chunk Size | Overlap |
|---|---|---|
| General RAG | 200-500 tokens | 10-20% |
| Code | 100-300 tokens | 20% |
| Dense technical | 300-600 tokens | 15% |
| Conversational | 150-300 tokens | 10% |
Starting Point: 250 tokens (~1000 characters)
✓ More accurate retrieval ✓ Specific matching ✗ Less context ✗ May miss connections
✓ More context ✓ Better comprehension ✗ Less precise retrieval ✗ Higher token costs
Good Chunking:
Poor Chunking:
Chunking strategies are implementation techniques, free to use.