Chunking Strategies for Structured Data in RAG Systems
Six strategies for making tables and relational data retrievable through RAG, from row-level chunks to hybrid RAG and text-to-SQL routing.
RAG systems often struggle with structured data like tables and CSV exports because standard chunking methods assume textual prose. A key issue is that a table with 50 rows gets compressed into a single chunk, losing context and making specific queries unanswerable. For instance, a user asking "What is the city with ID=5?" cannot be answered accurately when the chunk mixes data from all rows.
The article outlines six chunking strategies tailored for structured data:
1. **Row-Level Chunking** treats each row as its own chunk, embedding schema context. This yields high precision for point queries but creates an explosion of chunks at scale and cannot answer aggregation queries like COUNT or SUM.
2. **Small-Group Chunking** (3-10 rows per chunk) groups rows by shared attributes like region or category. While it reduces the number of chunks and enables within-group comparisons, it risks embedding dilution and may cause cross-group query failures.
3. **Schema-Aware Semantic Chunking** creates multiple layers of chunks: a schema summary, a summary of key metrics, and individual row details. This approach answers metadata questions and complements row-level or group chunking but requires ongoing schema maintenance.
Each strategy has trade-offs, with row-level chunking being best for small tables and schema-aware chunking ideal for large data catalog needs. The choice depends on the specific use case and data characteristics.
Written by urgent.news from HackerNoon's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
