RAG Explained in 1 Hour 🔥 | Build Your First GenAI Application from Scratch
Jeevitha Ramsudha
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RAG Explained in 1 Hour 🔥 | Build Your First GenAI Application from Scratch
6 116 просмотров · 5 дн. назад
Jeevitha Ramsudha
2,23 тыс. подписчиков
6 116 просмотров · 5 дн. назад
🚀 *RAG for Beginners in 1 Hour | Build Your First GenAI Application from Scratch*
notes: https://colab.research.google.com/dri...
Want to learn Retrieval-Augmented Generation (RAG) from scratch? In this video, you'll learn how RAG works, why it is important in Generative AI, and how it helps Large Language Models (LLMs) answer questions using external knowledge.
Whether you're a beginner in AI, Machine Learning, or Generative AI, this tutorial will help you understand the fundamentals of RAG with practical examples.
🔥 *What You'll Learn:*
✅ What is RAG (Retrieval-Augmented Generation)?
✅ Why do we need RAG in Generative AI?
✅ RAG vs Traditional LLMs
✅ Understanding the RAG architecture
✅ Documents and text chunking
✅ Embeddings and semantic search
✅ Vector databases and vector stores
✅ Document retrieval and context augmentation
✅ How LLMs generate answers using retrieved context
✅ Introduction to LangChain and RAG pipelines
✅ Real-world applications of RAG
🎯 *Who Should Watch This Video?*
Beginners learning Generative AI and LLMs
Python developers exploring AI applications
Data Science and Machine Learning enthusiasts
Students preparing for GenAI interviews
Anyone interested in building AI-powered applications
💡 *Why Learn RAG?*
RAG is a powerful technique for building AI applications that can answer questions from PDFs, documents, knowledge bases, and other external data sources. It helps connect LLMs with relevant information and can reduce unsupported answers when retrieval and generation work well.
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📺 *Explore More Topics on This Channel:*
Generative AI | Large Language Models (LLMs) | Prompt Engineering | LangChain | RAG Projects | AI Agents | Machine Learning | Deep Learning
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