Agentic RAG: AI agents now decide when and how to retrieve information

Agentic Retrieval-Augmented Generation (RAG) transforms retrieval from a fixed step into a decision-making process for AI agents. Unlike traditional RAG, which retrieves once and generates, agentic RAG allows the AI to evaluate the quality of retrieved information and re-attempt retrieval if necessary. This iterative approach, incorporating patterns like Corrective RAG and Self-RAG, enhances the AI's ability to handle complex queries that require multiple retrieval steps or comparison of information from different sources. Frameworks like LangGraph are well-suited for implementing these agentic RAG systems due to their ability to handle conditional logic and loops, though this increased sophistication comes at the cost of higher token usage and latency. AI

IMPACT Enhances AI's ability to handle complex queries by making retrieval an iterative, decision-based process.

RANK_REASON The item discusses a new pattern/technique for implementing RAG systems, highlighting its use with specific frameworks.

Read on dev.to — LLM tag →

• Agentic RAG
• Corrective RAG
• langgraph
• retrieval-augmented generation
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

• product
• infra

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