What is RAG(Retrieval Augmented Generation) | RAG Fundamentals And Advanced Techniques | Simplilearn

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The Retrieval-Augmented Generation (RAG) Course is your gateway to mastering one of the most impactful techniques in generative AI. In this course, you'll learn how RAG blends retrieval systems with language generation models to create AI outputs that are more accurate, grounded, and up-to-date. Starting with the core concept of RAG, we’ll build your understanding of its architecture, components, and practical applications. You’ll also explore how RAG compares to CAG (Cache-Augmented Generation), and when to use each approach. We’ll then dive into the importance of web crawling — how to gather high-quality knowledge sources for retrieval — and introduce tools like Crawl4AI that help you build robust RAG pipelines. Whether you're a beginner in AI or an experienced developer building intelligent applications, this course gives you both the theory and practical tools to work with Retrieval-Augmented Generation confidently

00:00:00 Introduction to Retrieval-Augmented Generation (RAG)
00:27:00 Splitting of the document
00:50:30 Maximum Marginal Relevance
00:01:11 Future of RAG

Retrieval-Augmented Generation (RAG) is a hybrid AI approach that enhances the performance of language models by combining two capabilities: retrieving external information from a knowledge base and then generating text based on that information. Traditional language models rely solely on their internal parameters and training data, which can lead to outdated or incorrect responses. RAG solves this by pulling in fresh, relevant data during inference. The RAG architecture includes two main parts: a retriever, which searches a document database for relevant content, and a generator, usually a large language model like GPT, which produces the final response using both the retrieved content and the input prompt. This makes RAG especially useful for tasks like open-domain question answering, technical support, and domain-specific chatbots, where factual accuracy and up-to-date information are critical.

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