RAG Pipeline Chunking Strategies: Split Documents for Better Retrieval

Short summary

RAG pipeline failures in production typically trace to chunking decisions during ingestion, not the embedding model or LLM. Four strategies exist—fixed-size, semantic, structural, and hierarchical—each trading coverage against retrieval precision. Start with 256–512 tokens at 10–15% overlap for general prose, evaluate against a golden retrieval set (80%+ recall@3), then adjust based on your corpus type and query profile.

• •Most RAG failures in production stem from poor chunking, not the model or embedding engine
• •Fixed-size chunking (256-512 tokens) is the pragmatic starting point; semantic/structural chunking outperforms on technical docs
• •Hierarchical chunking (small chunks for vector retrieval, large parent chunks for full context) is the highest-performance approach for production systems

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