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- 01 Semantic chunking significantly improves retrieval accuracy over fixed-size splitting but is considerably slower
- 02 Late Chunking and Contextual Retrieval are the most notable experimental approaches of 2025-2026
- 03 Multilingual projects require dedicated embedding models (multilingual-e5-base, bge-m3)
- 04 Chonkie excels in speed and multi-strategy support, while LlamaIndex leads in hierarchical retrieval
+ What is RAG chunking and why does it matter?
Chunking is the process of breaking large text into smaller pieces. In a RAG pipeline, bad chunks lead to bad embeddings, and bad embeddings lead to irrelevant retrieval results.
+ Which chunking strategy should I use?
For quick prototypes, use fixed-size or recursive. If accuracy is critical, use semantic + contextual retrieval. For code repos, use AST-based. For PDFs with tables, use vision-based (ColPali).
+ What is the difference between semantic chunking and fixed-size chunking?
Fixed-size chunking splits text by a fixed token count and ignores semantic boundaries. Semantic chunking uses embedding similarity to detect topic boundaries but runs 5-10x slower.
+ Which embedding model should I use for non-English RAG projects?
Multilingual embedding models are required. intfloat/multilingual-e5-base offers balanced size and performance. BAAI/bge-m3 stands out with dense + sparse + colbert support.
+ How can I evaluate my RAG chunking strategy?
Use the RAGAS framework to measure hit rate, MRR, context precision, and faithfulness metrics. Run A/B tests after changing your strategy to objectively compare approaches.