From the course: Building a RAG Solution from Scratch
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What is Retrieval-Augmented Generation (RAG)?
From the course: Building a RAG Solution from Scratch
What is Retrieval-Augmented Generation (RAG)?
- [Narrator] RAG, or retrieval augmented generation, is a way to enhance the capabilities of language models by combining two key elements, retrieval and generation. Together, they create responses that are both informed by relevant data and fluently generated for conversational fail. Let's break down why this combination is so powerful. While some large language models are great at generating natural language, they sometimes lack specific knowledge. For example, a standalone LLM might not have access to the latest medical research or company policies. RAG solves this by retrieving relevant information on demand, then generating a response based on that data. It's like giving the LLM an up-to-date library. It can consult in real time and really fast. To understand RAG, I like to think of it as a library with a very knowledgeable librarian. The retrieval model is the library where all the relevant information lives. It finds the right facts or documents based on the user's question…
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