Chunking is a critical step in RAG pipelines, directly impacting retrieval quality and downstream generation. This guide covers 11 distinct strategies, including fixed-size chunking, recursive splitting, document-aware segmentation, semantic chunking, and agent-based methods. Each approach is evaluated for use cases such as code, prose, or structured data. For engineers building production RAG systems, understanding these trade-offs is essential. The original source is a WeChat article, but the content is repackaged here for broader accessibility. We recommend using this as a reference when designing your chunking pipeline, but always test against your specific data and retrieval metrics.
A comprehensive overview of 11 chunking methods for RAG systems, from fixed-size to semantic and agent-based approaches.