Retrieval-augmented generation systems depend on how source documents are split into chunks. A recent Machine Learning Mastery article argues that choosing the right chunking strategy is not a matter of picking a default, but of working through a structured decision tree.
The article walks developers through a sequence of decisions to match chunking to their RAG setup. Instead of guessing at chunk size or splitting rules, the decision-tree approach forces explicit consideration of the application's needs and constraints.
The result is a practical framework for a step that is easy to overlook but central to retrieval performance. For teams building RAG pipelines, the guide provides a clear starting point for making chunking an intentional design choice.