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In this tutorial, we'll explore how RAG works, why it’s essential for modern AI, and how it solves the limitations of traditional language models. Whether you're a developer, tech enthusiast, or just curious about AI advancements, this guide will show you how RAG is shaping the future of AI by making models smarter and more relevant.RAG allows AI models to search for real-time information from external sources, combine it with their existing knowledge, and generate accurate, up-to-date responses. It’s like giving AI the ability to "Google" for answers while keeping their knowledge sharp.
00:00 Introduction
-- Frequently Asked Questions
✅ Question 1 What is Retrieval Augmented Generation (RAG)?
Answer:RAG is a hybrid AI technique that combines two key processes: retrieving external data from a knowledge base or web and then generating a response based on both the retrieved information and the model’s pre-trained knowledge. This allows the model to provide more accurate and up-to-date answers compared to traditional language models that only rely on their training data.
✅ Question 2 How does RAG improve the accuracy of AI models?
Answer:RAG enhances accuracy by pulling in relevant, real-time information from external sources. Traditional language models can only answer questions based on data they were trained on, which can become outdated. With RAG, AI models can retrieve the latest information, combine it with what they already know, and generate a response that’s both current and highly informed.
✅ Question 3 What are the main applications of RAG?
Answer:RAG is used in various fields such as customer support, healthcare, finance, and legal research. It powers AI models to deliver real-time, accurate responses by retrieving the most relevant and recent data. For instance, it’s used in chatbots for better customer service, in healthcare for retrieving the latest research, and in search engines to provide more relevant search results.
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➡️ About Post Graduate Program In AI And Machine Learning
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