LLMs get a real-time upgrade with dynamic data retrieval through AI agents.
Very useful survey paper.
Agentic RAG enhances LLMs by integrating autonomous agents for dynamic data retrieval and workflow optimization. This overcomes limitations of static RAG systems in handling complex queries and multi-step reasoning. Agentic RAG systems excel in various applications like customer support, healthcare, and finance, showing improved accuracy and adaptability.
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https://arxiv.org/abs/2501.09136
Original Problem 😲:
→ LLMs struggle with dynamic, real-time queries due to reliance on static training data, leading to outdated or inaccurate responses.
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Methods discussed this Paper 🤔:
→ Agentic RAG integrates AI agents into the RAG pipeline.
→ These agents use agentic design patterns (reflection, planning, tool use, multi-agent collaboration) to dynamically manage retrieval strategies, refine contextual understanding, and adapt workflows.
→ This enables handling of complex task requirements and multi-step reasoning.
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Key Insights from this Paper 💡:
→ Agentic RAG surpasses traditional RAG by enabling dynamic retrieval and adaptation.
→ Agents orchestrate retrieval, filter information, and refine responses.
→ This improves accuracy and adaptability for complex tasks.
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