Retrieval augemted Generation(Rag) Part -1

Description

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. ⸻ 📘 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. ⸻ 🔥 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 ⸻ 🧠 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 It allows AI systems to access external knowledge dynamically — meaning no more outdated answers or hallucinations! ⸻ 👩‍💻 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 ⸻ 📈 Keep Learning with Soumya AI Vision 🔔 Subscribe for weekly videos on GenAI, RAG, Agents, and AI Tools 💬 Comment below your next topic — “RAG + LangChain”, “Agentic Framework”, or “Vector DB Explained”? 🎙️ Don’t miss our AI Podcast Series, where we discuss trending AI use cases and developer insights