Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

Understand

Large Language Models (LLMs) have advanced artificial intelligence by enabling human-like text generation and natural language understanding.

• However, their reliance on static training data limits their ability to respond to dynamic, real-time queries, resulting in outdated or inaccurate outputs.
• Retrieval-Augmented Generation (RAG) has emerged as a solution, enhancing LLMs by integrating real-time data retrieval to provide contextually relevant and up-to-date responses.
• Despite its promise, traditional RAG systems are constrained by static workflows and lack the adaptability required for multi-step reasoning and complex task management.