Understanding Agentic RAG and How It’s Different From RAG With Code

Author(s): Harsh Maheshwari

Originally published on Towards AI.

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In the world of Large Language Models (LLMs), Retrieval Augmented Generation (RAG) has emerged as a game-changer. Traditional RAG, while groundbreaking, often follows a predictable pattern: fetch information based on a user’s query, then use that information to generate a response. But what if we could make this process more intelligent, more adaptable? Enter Agentic RAG, a significant leap forward that’s as different from traditional RAG as a smartphone is from a flip phone.

Imagine asking a customer service chatbot a complex question that requires information from multiple sources. Traditional RAG might struggle to provide a comprehensive answer, but Agentic RAG, with its AI-powered agents, can navigate through knowledge bases, product manuals, and customer history to deliver a personalized solution. That’s the power of Agentic RAG — it transforms LLMs from passive information processors into active knowledge seekers.

RAG enhances LLMs by combining their generative power with the ability to access and retrieve relevant information from external knowledge sources. This approach allows LLMs to generate more informed and comprehensive responses, grounded in factual knowledge and real-world data.

A typical RAG system involves the following steps:

Question Encoding: The user’s query… Read the full blog for free on Medium.

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Published via Towards AI