RAG Is Smart — But Agentic RAG with LangGraph Is Smarter: A Practical Guide
SMRTR summary
Agentic RAG enhances traditional Retrieval-Augmented Generation by enabling AI to solve complex, multi-step problems. Unlike standard RAG systems that follow a fixed question-answer flow, Agentic RAG can break tasks into steps, make decisions, and interact with external tools like APIs. For example, when asked about weekend activities in Paris, it can check real-time weather data, plan accordingly, and provide a contextualized response. This approach allows AI to handle ambiguous queries and deliver more useful, dynamic answers.
SMRTR provides this summary for quick context. The original article belongs to GitConnected.
Read the original article