Chunking Methods on Retrieval-Augmented Generation - Effectiveness Evaluation Against Computational Cost and Limitations
A systematic evaluation of chunking strategies in RAG systems addresses a critical gap in LLM infrastructure. While fixed-size and semantic chunking dominate production systems, emerging methods proliferate with narrow validation and unclear trade-offs between retrieval quality and computational overhead. This first comparative study matters because chunking directly impacts both retrieval accuracy and inference cost, yet practitioners lack principled guidance on method selection across diverse data types and use cases. The findings will shape how teams architect retrieval pipelines at scale.62
