🚀 Multimodal Retrieval-Augmented Generation (RAG) combines the power of retrieval-based models with generative transformers to create more accurate, context-aware, and real-time AI responses. In AI/ML/DL Masterclass Session 228, we explore how RAG architectures are transforming Generative AI, NLP, and Multimodal Intelligence.

🔍 What You’ll Learn in this Session
✅ What is Retrieval-Augmented Generation (RAG)?
✅ How Multimodal RAG works with text, image, audio & video
✅ Key components: retriever + generator pipeline
✅ Real-world applications in QA systems, chatbots, and search engines
✅ Tools like LangChain, FAISS, Pinecone, HuggingFace Transformers
✅ Future of scalable RAG with foundation models

💡 Why RAG is a Game-Changer in AI
RAG enhances context awareness, factual accuracy, and domain adaptation, making it ideal for enterprise AI, personal assistants, and search systems. It bridges the gap between static memory and dynamic knowledge access.

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