Beyond Fixed-Size: A Deep Dive into Modern Document Chunking for RAG TL;DR For those of you short on time, here’s the key takeaway: document chunking, the process of breaking down documents for Retrieval-Augmented Generation (RAG) systems, has grown up. We've moved far beyond simple fixed-size text splitting. Today, the best approach is to use sophisticated, context-aware strategies that understand a document's structure and meaning. There is no "one-size-fits-all" chunking solution. The optimal strategy depends entirely on your document type, your industry, and what you're trying to achieve. The modern toolkit is incredibly rich, featuring specialized models on HuggingFace, powerful open-source libraries like Unstructured.io and LangChain, and scalable enterprise platforms from Google, AWS, and Azure. The winning formula right now is a hybrid approach—combining the speed of classic NLP with the deep understanding of transformer models. And for anyone working in...
Cranking out good code