Retrieval-Augmented Generation (RAG)

What is

in LLMs

Image Credit: Analytics Drift

Introduction to RAG

Unveiling Retrieval-Augmented Generation (RAG): A methodology that combines the power of retrieval from databases with generative language models.

How RAG Works

RAG works by retrieving relevant documents or data and then generating responses based on both the retrieved information and its own knowledge.

The Advantages of RAG

Enhanced Accuracy: By retrieving up-to-date or specific information, RAG provides more accurate and relevant responses.

Advantages - #2

Contextual Understanding: RAG models can provide responses that consider broader context from the retrieved documents, offering deeper insights.

Advantages - #3

Dynamic Learning: Unlike static models, RAG can leverage the latest data, adapting to new information and trends over time.

Disadvantages of RAG

Complexity in Integration: Implementing RAG can be complex, requiring robust systems to handle both retrieval and generation processes.

Disadvantages - #2

Dependency on Source Quality: The accuracy of RAG's output heavily relies on the quality and reliability of the source documents it retrieves.

Disadvantages - #3

Computational Overhead: The two-step process of retrieval and generation requires significant computational resources, potentially limiting scalability.

RAG in Practice

RAG is used in various applications, from enhancing chatbots to providing detailed and sourced responses in knowledge work.


Retrieval-Augmented Generation represents a significant advancement in LLMs, offering a blend of retrieved knowledge and generative creativity.

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Produced by: Analytics Drift Designed by: Prathamesh