RAG, Agentic RAG, Graph RAG: A Guide to Choosing the Right LLM Architecture

Standard Retrieval-Augmented Generation (RAG) has long been the default method for grounding large language models in real-world data by embedding queries and retrieving relevant document chunks. However, as use cases grow more complex, basic RAG struggles with multi-hop reasoning, tool calls, and relationships spread across large document sets. Two more advanced approaches have emerged to address these gaps: Agentic RAG, which incorporates autonomous tool use and iterative reasoning, and Graph RAG, which maps relationships between entities in a knowledge graph. Each architecture is designed to solve a distinct set of problems, and selecting the wrong one can lead to costly rebuilds. Understanding the specific strengths and limitations of all three approaches is now essential for developers building production-grade LLM applications.