Learning path · Embeddings & Representation · 39
Semantic Chunking
Splitting text at natural topic boundaries detected by embedding similarity shifts between sentences or paragraphs.
Why it matters
- Keeps related ideas together better than blind fixed windows.
- Reduces fragments that confuse both retriever and reader model.
- Costs extra embedding passes during indexing.
Key ideas
- Breakpoint detection
- Coherence scoring
- Variable chunk sizes
Semantic chunking scans sentences, measuring embedding drift between neighbours; when similarity drops sharply, start a new chunk. Compared to fixed windows, it preserves topical units—helpful for policies and essays. Compared to structure-aware chunking, it ignores headings and tables unless text order reflects them. Use when documents lack reliable markup but read linearly; validate on Precision@K because overtly long semantic segments can dilute matches. Profile indexing cost on your largest tenants before rolling semantic chunking fleet-wide; variable segment sizes affect storage nonlinearly.
Updated 2026-08-09 · Full learning path