From Regex to Vision Models: Which RAG Technique Fits Which Problem

Most Retrieval-Augmented Generation (RAG) problems do not require the classic playbook of chunking, embedding, and vector search. Instead, RAG problems can be categorized along two primary axes: document complexity and question control. Document complexity ranges from fixed templates best handled by regex to visually rich content requiring vision models. This diagnostic framework helps practitioners select the appropriate technique for their specific use case, avoiding the unnecessary cost and complexity of a one-size-fits-all approach.