Welcome to the Era of Self-RAG and Agentic RAG
Last Updated on April 15, 2025 by Editorial Team
Author(s): Kalash Vasaniya
Originally published on Towards AI.
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Recall the good old RAG (Retrieval-Augmented Generation)?
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.
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.
Now, let’s look at Agentic RAG, a… Read the full blog for free on Medium.
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