RAG Explained For Beginners | What Is RAG | RAG Tutorial | Retrieval Augmented Reality | Simplilearn

Simplilearn · Beginner ·🔍 RAG & Vector Search ·2mo ago

Skills: RAG Basics90%

Key Takeaways

Explains RAG basics using retrieval augmented generation and reality concepts

Original Description

🔥Applied Generative AI Specialization, delivered by Simplilearn in collaboration with Purdue University - https://www.simplilearn.com/applied-ai-course?utm_campaign=HkUs8mb0k3s&utm_medium=DescriptionFirstFold&utm_source=Youtube 🔥Partnership is with E&ICT of IIT Kanpur - Professional Certificate Course in Generative AI and Machine Learning - https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=HkUs8mb0k3s&utm_medium=DescriptionFirstFold&utm_source=Youtube 🔥Partnership is with IITM Pravartak - Advanced Executive Program In Applied Generative AI - https://www.simplilearn.com/applied-generative-ai-course?utm_campaign=HkUs8mb0k3s&utm_medium=DescriptionFirstFold&utm_source=Youtube This video introduces the concept of RAG (Retrieval-Augmented Generation) in simple terms. It explains how traditional AI tools, like large language models (LLMs), struggle with providing accurate answers when dealing with company-specific, private documents like contracts, internal policies, or reports. RAG solves this by enabling the AI to first retrieve relevant information from a set of documents before generating an answer. The process involves key steps like chunking (breaking documents into smaller pieces), embeddings (converting text into numerical representations), and using a vector database to store and search for meaning-based information efficiently. We will explore how RAG helps AI systems perform semantic search (finding information based on meaning rather than exact keywords), augmenting the AI's responses with relevant document chunks, and generating grounded answers. You'll also learn why RAG is often preferred over fine-tuning for private, frequently updated documents, making it ideal for use in applications like company knowledge assistants, customer support bots, legal document assistants, and more. By the end of the video, you'll have a clear understanding of how RAG works, why it's a powerful tool for AI systems, and

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