RAG Explained: How it Works, Job Roles, & Salary Insights | Kannada Guide | MicroDegree

Published: 29 October 2024
on channel: MicroDegree ಕನ್ನಡ
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🤖 Uncover the Power of RAG AI! 🌟 Learn how Retrieval-Augmented Generation boosts response accuracy, key roles, and high-paying opportunities in AI! 💼📈🚀

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🔍 What is RAG (Retrieval-Augmented Generation)? 🤖 I’m Shwetha from MicroDegree, and in this video, we’ll uncover the advanced AI technique, RAG, and why it’s transforming the way machines answer complex questions. 🚀 Learn about the three core steps: Input, Augmented Retrieval, and Generation, which power more accurate responses. 🧠✨
We'll dive into real-life applications of RAG, like customer support, answering questions, and summarizing complex information. Plus, explore exciting career paths such as Machine Learning Engineer, AI Researcher, and Software Developer, and discover salary insights for various experience levels. 💼🌐
👉 Like 👍, subscribe 🔔, and drop your questions in the comments! Let’s explore the future of AI together! 🌟

🎥 ನಮಸ್ಕಾರ! I’m Shwetha, ಈ ವೀಡಿಯೋಗೆ ನಿಮಗೆ ಸ್ವಾಗತ! 😊 ಇಂದಿನ ವೀಡಿಯೋದಲ್ಲಿ RAG (Retrieval-Augmented Generation) ಅನ್ನು, ಮತ್ತು ಇದನ್ನು AI ಕ್ಷೇತ್ರದಲ್ಲಿ ಹೇಗೆ ಬಳಸಲಾಗುತ್ತಿದೆ ಎಂಬುದನ್ನು ತಿಳಿಯಲಿದ್ದೇವೆ! 🚀 RAG ಮೂಲಕ, machines‌ ಗಳು data retrieve ಮಾಡಿ, complex questions ಗೆ accurate answers provide ಮಾಡುತ್ತವೆ. 🧠✨
ನಾವು RAG ನ real-life use cases ಅಂದರೆ customer support, Q&A systems, ಮತ್ತು summarizing large topics ಗಳಲ್ಲಿ ಹೇಗೆ implement ಮಾಡಬಹುದು ಎಂಬುದನ್ನು ಕಳಿಯುತ್ತೇವೆ. ಜೊತೆಗೆ, Machine Learning Engineer, AI Researcher, Software Developer ಮುಂತಾದ roles ಮತ್ತು salary insights ಗಳು ಎಷ್ಟು ಮತ್ತು ಹೇಗೆ ಇವೆ ಎಂಬುದರ ಕುರಿತು ತಿಳಿಯಿರಿ. 💼🌐
👉 Like 👍 ಮಾಡಿ, subscribe 🔔 ಮಾಡಿ, ಮತ್ತು ನಿಮ್ಮ ಪ್ರಶ್ನೆಗಳನ್ನು comments ನಲ್ಲಿ ಹಂಚಿಕೊಳ್ಳಿ! 💬

🎥✨ Ready to dive into the world of RAG (Retrieval-Augmented Generation)? 🤖 In this video, we’ll break down RAG and see why it’s a powerful AI approach that enhances response accuracy by retrieving relevant data before generating answers! 🌐💡

Let’s start with the basics: RAG works through three main steps — Input, Augmented Retrieval, and Generation. First, it takes user input, then retrieves relevant data to improve the response, and finally generates a more accurate and relevant answer. 🔍📊

Learn where RAG is applied! From customer support and question-answering to summarizing complex topics, RAG helps provide reliable, efficient responses. 📞💼

Curious about the career opportunities? 🚀 Roles like Machine Learning Engineer, AI Researcher, and Software Developer work closely with RAG, with entry-level salaries starting around ₹6-10LPA, and experienced professionals earning up to ₹30LPA or more! 💸💻

👉 Don’t forget to like 👍, subscribe 🔔, and share your thoughts in the comments! 💬 Let’s explore the potential of RAG in AI together! 🌟



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