Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

📰 ArXiv cs.AI

Agentic Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) with real-time data retrieval for more accurate outputs

advanced Published 2 Apr 2026

Action Steps

Understand the limitations of static training data in LLMs
Integrate real-time data retrieval using RAG to enhance LLM performance
Apply Agentic RAG to enable more accurate and contextually relevant outputs
Evaluate the impact of Agentic RAG on LLM-based applications

Who Needs to Know This

AI engineers and researchers benefit from understanding Agentic RAG to improve LLM performance, while product managers can leverage this technology to develop more effective language-based products

Key Insight

💡 Agentic RAG overcomes the limitations of static training data in LLMs by integrating real-time data retrieval

Key Takeaways

Agentic Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) with real-time data retrieval for more accurate outputs

Full Article

Title: Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

Abstract:
arXiv:2501.09136v4 Announce Type: replace Abstract: 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their reliance on static training data limits their ability to respond to dynamic, real-time queries, resulting in outdated or inaccurate outputs.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 relevan

Read full paper → ← Back to Reads