RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem

Treating Retrieval-Augmented Generation (RAG) as a machine learning problem is a costly misconception that leads development teams down the wrong path. Unlike machine learning which predicts unknown outcomes, RAG retrieves existing answers from documents, meaning failures are traceable engineering bugs rather than statistical errors. Applying standard ML techniques like automated hyperparameter tuning for 'chunk size' is ineffective because the optimal settings depend on document structure and question type, not a statistical search. System improvement comes from engineering better components—such as parsing, retrieval precision, and prompting—not from training a model on more data.