Скачать или смотреть Retrieval augemted Generation(Rag) Part -1

In this video, we’ll dive into Retrieval Augmented Generation (RAG) – Part 2, where you’ll learn how to implement RAG in real-world projects using Generative AI frameworks. 🚀
We’ll go beyond the theory — and build a working RAG pipeline that retrieves external data, embeds it into a vector database, and generates context-aware answers using LLMs like OpenAI GPT models.

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📘 About Soumya AI Vision

Welcome to Soumya AI Vision, your one-stop channel for mastering Generative AI, RAG systems, Agentic Frameworks, and real-world AI innovations.
Here, you’ll learn through hands-on tutorials, projects, and podcasts — designed to make you an AI engineer ready for the future.

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🔥 What You’ll Learn in This Video:

✅ Full RAG Architecture (Retriever → Vector DB → Generator → Response)
✅ Implementing RAG with LangChain, FAISS, and OpenAI APIs
✅ Understanding embeddings and contextual retrieval
✅ Building your own knowledge-based AI assistant
✅ Optimization techniques for faster and cheaper responses
✅ Real-world deployment insights

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🧠 Why RAG Matters

Retrieval Augmented Generation bridges the gap between LLM knowledge and real-world data.
It allows AI systems to access external knowledge dynamically — meaning no more outdated answers or hallucinations!

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👩‍💻 Who Should Watch This:
• Developers learning Generative AI development
• AI & ML enthusiasts building smart chatbots
• Students exploring LLMs and RAG architecture
• Professionals building knowledge-driven assistants
• Anyone who wants to understand how ChatGPT-style retrieval really works

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📈 Keep Learning with Soumya AI Vision

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