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.

RAG Chunking: Strategies, Limitations, and Decision Map

TL;DR

There are 12 different chunking strategies in RAG systems. The right choice depends on content type, budget, and quality requirements. This guide covers the pros/cons of each strategy and provides a decision map for choosing the right one.

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