build your own rag chatbot | generative ai tutorial for beginners (llm, embeddings, vector db)

Description

Where is Generative AI actually used in real systems like chatbots and enterprise search? If you can explain this clearly, you are already ahead in your Gen AI journey and real-world understanding. Understanding how RAG (Retrieval Augmented Generation) works is one of the most practical and high-impact concepts in modern Generative AI and LLM-based systems. This video is part of a complete Generative AI fundamentals series (Start Here), where we move beyond theory and show how Gen AI is used in real-world applications. In this video, we break down how to build a RAG chatbot step by step — from using your own documents as a knowledge base, to converting data into embeddings, to storing them in a vector database, and finally generating accurate answers using LLMs and prompt engineering. If you are an engineering student, software developer, data professional, or someone transitioning into AI, Data Science, or Machine Learning roles, this video will help you understand how modern AI systems like ChatGPT-style chatbots are actually built — and how to apply these concepts in real projects. We use a structured whiteboard-style visual approach to simplify complex architectures, making them easy to understand while still keeping them technically accurate. --- 🚀 In this video, you will learn: How to build a chatbot using your own data What is RAG (Retrieval Augmented Generation) How document chunking works in AI systems What are embeddings and how they represent meaning How vector databases store and retrieve data How similarity search works in practice How LLMs use context to generate accurate answers How prompt engineering improves output quality Complete end-to-end RAG chatbot architecture Real-world use cases of RAG in enterprise systems --- This video is a must-watch for anyone working or aspiring to work in: Data Science Machine Learning Artificial Intelligence Software Development Generative AI and LLM-based systems AI Engineering Backend and system design for AI --- Whether you are an engineering student in India, a working professional in the US, UK, Europe, or Australia, or someone exploring AI for practical applications, this series will help you build strong fundamentals and understand how AI systems are designed in the real world. --- 🔖 Hashtags #GenerativeAI #GenAI #RAG #RAGChatbot #LLM #VectorDatabase #Embeddings #PromptEngineering #ChatGPT #ArtificialIntelligence #MachineLearning #DataScience #AIProjects #AIEngineer #LearnAI #GenAIFundamentals