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Retrieval-Augmented Generation (RAG) | Prof. Anis Koubaa | MAI554 Deep Learning for Language Modeling Course In this lecture, Prof. Anis Koubaa explains the core concepts behind Retrieval-Augmented Generation (RAG) and why it's a game-changer for language modeling. He covers the key components of RAG and discusses why it is better than fine-tuning and pretrained models for certain applications. 🔍💡 Prof. Koubaa delves into the importance of vector stores for fast and efficient semantic retrieval, explaining how they enhance performance in natural language processing tasks. He also covers chunking and its critical role in data processing for RAG applications. 📦🧠 The lecture includes a detailed, practical example in Google Colab, where you’ll learn how to: • Perform chunking with LangChain • Implement a vector store using Chroma DB • Create and deploy a LangChain RAG chain • Build a domain-specific chatbot based on data collection Notebook: https://colab.research.google.com/dri... Don't miss out on learning about these cutting-edge techniques that make RAG a powerful tool for building sophisticated AI systems! 🎓🤖 🔔 Subscribe for more deep learning and AI tutorials! #DeepLearning #RAG #RetrievalAugmentedGeneration #AI #LanguageModeling #LangChain #VectorStore #ChromaDB #Chatbot #SemanticSearch #MachineLearning