RAG in AI

Hi Madhu,
Classic RAG (Retrieval-Augmented Generation) is a straightforward setup in which the model fetches pertinent data and provides an answer directly from them. It's a single shot normally — retrieve and then generate — and is fine for basic Q&A from a closed knowledge base.

Agentic RAG, in contrast, provides the model with more autonomy. It enables incremental reasoning by the system, to look ahead and figure out what it needs to retrieve, determine if additional retrievals are even needed and even backtrack and loop back for explanation or more information. Dynamic and iterative, rather than one-shot, responses and hence more appropriate for multi-part or complicated questions.

That is, Classic Retrieval-Augmented Generation (RAG) is essentially pulling and revealing information to an LLM and providing it to the model, whereas Agentic RAG adds AI agents to perform more complex functions like reasoning, decision-making, and dynamic context adaptation. Agentic RAG builds upon classic RAG by adding intelligence to the process, thus being proactive and adaptive, is this good enough? Tell me if you require a further explanation or examples to make this point clearer. Hope this short description will help.

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John Godel1y ago
Accepted answer
Hi Madhu,
Classic RAG (Retrieval-Augmented Generation) is a straightforward setup in which the model fetches pertinent data and provides an answer directly from them. It's a single shot normally — retrieve and then generate — and is fine for basic Q&A from a closed knowledge base.

Agentic RAG, in contrast, provides the model with more autonomy. It enables incremental reasoning by the system, to look ahead and figure out what it needs to retrieve, determine if additional retrievals are even needed and even backtrack and loop back for explanation or more information. Dynamic and iterative, rather than one-shot, responses and hence more appropriate for multi-part or complicated questions.

That is, Classic Retrieval-Augmented Generation (RAG) is essentially pulling and revealing information to an LLM and providing it to the model, whereas Agentic RAG adds AI agents to perform more complex functions like reasoning, decision-making, and dynamic context adaptation. Agentic RAG builds upon classic RAG by adding intelligence to the process, thus being proactive and adaptive, is this good enough? Tell me if you require a further explanation or examples to make this point clearer. Hope this short description will help.
+1