[What’s RAG?] Bridging Knowledge Gaps: The Role of Retrieval-Augmented Generation(RAG) in Enhancing AI

Discover how Retrieval-Augmented Generation (RAG) is transforming AI by integrating real-time data for more accurate and relevant responses. Learn about its impact across industries.

That’s right.

With an explosive entry of OpenAI and their revolutionary product, ChatGPT, the world of AI comes crashing into your life.

Still, though, many essential terms and practices go unseen by the new or casual user.

Retrieval-Augmented Generation(RAG) is a technique that enables large language models, such as ChatGPT, to access more relevant retrievable context along with the small portion that can be offered to them for training.

At its heart, RAG allows systems that would be useful in only a few narrow domains to be much more generally capable across many domains.

In this article, we’ll explore:

• What is the RAG?
• Why is RAG important
• How RAG Works
• Examples of use for RAG
• How to test the RAG approaches: reasons why…