How to Build Agentic RAG: A Step-by-Step Guide to Intelligent Retrieval-Augmented GenerationTaking Retrieval-Augmented Generation to the Next Level with Intelligent Agents

Author(s): Sai Bhargav Rallapalli

Originally published on Towards AI.

Using interrupt and conditional routing, escalate a request to a human expert

If you’ve worked with Retrieval-Augmented Generation (RAG), you know it’s a game-changer for enhancing Large Language Models (LLMs) by fetching relevant data before generating answers.

This article explores the evolution from traditional Retrieval-Augmented Generation (RAG) to the more dynamic and intelligent Agentic RAG, which enhances decision-making and flexibility by allowing agents to adapt in real time, refine queries, and evaluate relevance in data retrieval to improve the generation of responses. It outlines the limitations of conventional RAG, the ways Agentic RAG addresses these issues, and provides a step-by-step guide on how to build an intelligent retrieval system using LangGraph, highlighting its capabilities such as dynamic retrieval, self-correction, and modular scalability.

Read the full blog for free on Medium.

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Published via Towards AI