Welcome to the Era of Self-RAG and Agentic RAG

Author(s): Kalash Vasaniya

Originally published on Towards AI.

Source: From https://dev.to/akeshlovescience/rag-architecture-explained-beginner-5hn4

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Recall the good old RAG (Retrieval-Augmented Generation)?

Source: From https://dev.to/akeshlovescience/rag-architecture-explained-beginner-5hn4

It’s as if you had a librarian retrieve books suitable for your query, give them to you, and you take care of the rest. This is how it is done:

Acquires important documents.Re-ranks them in relevance orderGenerates a response based on the top matches.

Great for anchoring LLMs in out-of-model knowledge, reducing hallucinations, and improving accuracy.

But here’s the problem: It doesn’t change.

No feedback loops.No self-reflectionA one-and-done strategy

What if we had an AI that could reflect, refine, and improve its answers?

A brighter version of RAG that first verifies and enhances before giving answers.

Source: From https://www.linkedin.com/in/stevenouri

Say hello to Self-RAG, the future of AI. Unlike regular RAG, it evaluates itself before responding. If the retrieved information isn’t up to standards, it reformulates the query and attempts again before producing an answer.

Adds a validation layer before response generationPrevents misleading or irrelevant answersMakes LLMs more cautious and preciseSuitable for noisy data, uncertain questions, and multi-hop reasoning

How can Self-RAG revolutionize how AI engages with intricate problems?

An advanced AI that strategizes, adapts, and makes decisions autonomously.

Source: From https://www.linkedin.com/in/stevenouri

Now, let’s look at Agentic RAG, a… Read the full blog for free on Medium.

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Published via Towards AI