RAG vs. Agentic RAG vs. Graph RAG: Which One Actually Fits Your Use Case?

RAG pipelines ground LLMs in real data by embedding queries and retrieving relevant document chunks. While naive RAG suffices for simple tasks, it fails during multi-hop reasoning or complex entity relationships. This limitation has led to Agentic RAG and Graph RAG, which address different structural problems. Additionally, tools like AutoRAG and RAGBuilder help optimize parsers, chunk sizes, and embedding models to improve accuracy and reduce costs, though they may share specific OCR blind spots during processing.