From the course: Securing Generative AI: Strategies, Methodologies, Tools, and Best Practices

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Introducing retrieval augmented generation (RAG)

Introducing retrieval augmented generation (RAG)

From the course: Securing Generative AI: Strategies, Methodologies, Tools, and Best Practices

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Introducing retrieval augmented generation (RAG)

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- [Instructor] One of the most popular topics nowadays in AI is retrieval augmented generation, and I would like to actually go over what it is, what entails, and what are the advantages that you have with retrieval augmented generation. What you're seeing in front of you, of course, is one of my articles in my personal blog that we just mentioned earlier. This one specifically is around LangChain, and I wrote it several months ago. However, what I would like to do is take advantage of this diagram here, right? So let me actually start by defining what is RAG or retrieval augmented generation. So RAG, basically, so RAG or retrieval augmented generation, right, is basically, a machine learning, an AI concept that aims to enhance the capabilities of gen AI models with external knowledge sourced from either a document collection, you know, another database, and so on. And basically act as a framework that is aimed to enhance the quality of the responses, basically for you to get better…

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