chunking-strategy

Chunking documents for RAG involves dividing large texts into meaningful segments that preserve semantic boundaries. Start with fixed-size chunks (512 tokens) and 10–20% overlap, then evaluate retrieval precision and recall to refine your strategy based on end-to-end accuracy.

Semantic chunking preserves meaning across segment boundaries, improving embedding quality for vector databases. Available strategies include fixed-size, recursive, structure-aware, and semantic approaches; select based on your document type, token budget, and retrieval accuracy requirements.

Chunk size and overlap balance context preservation with computational efficiency. Configure token limits and overlap percentages during preprocessing, then validate against retrieval performance metrics. Larger overlaps reduce information loss but increase indexing costs.

Structure-aware chunking strategies maintain document hierarchy and formatting during segmentation. This approach ensures metadata, headers, and semantic relationships are retained, improving retrieval precision when searching across complex documents in vector databases.

Fixed-size chunking divides documents by token count for simplicity and speed, while semantic chunking respects meaning boundaries for higher embedding quality. Semantic approaches yield better retrieval accuracy but require additional computational overhead during preprocessing.

Evaluate chunking by measuring retrieval precision, recall, and end-to-end accuracy against your RAG pipeline. Test multiple strategies and configurations, comparing how chunk size, overlap, and structural preservation impact search quality and embedding relevance.