Build a RAG Application from Scratch | RAG Tutorial for Beginners

Learn how to build and understand a RAG (Retrieval-Augmented Generation) application in this beginner-friendly AI Engineering tutorial. Explore how documents, vector databases, and Large Language Models (LLMs) work together to retrieve relevant information and generate context-aware AI responses. This tutorial is part of the RAG Series by Reactive Codes, designed for beginners learning Artificial Intelligence, LLM applications, and modern AI Engineering concepts. *What You’ll Learn* What Retrieval-Augmented Generation (RAG) is How a RAG pipeline works How documents are processed and used as context The role of vector databases in RAG How an LLM uses retrieved information to generate answers How the different components connect in a RAG application *Why Learn RAG?* RAG is an important architecture for building AI applications that answer questions using external documents and knowledge sources. Understanding its workflow provides a foundation for exploring document question-answering systems, knowledge assistants, and other LLM-powered applications. *Who Should Watch?* This video is for AI Engineering beginners, students learning Large Language Models, aspiring AI developers, and anyone interested in understanding how RAG applications work. *Continue Learning* Explore the AI Engineering series and follow the RAG Series for more beginner-friendly explanations of AI concepts and practical applications. #RAG #AIEngineering #RetrievalAugmentedGeneration 🔗 𝗟𝗶𝗻𝗸𝘀: WhatsApp → https://chat.whatsapp.com/F2K07pSIL20... Reactive Codes → https://reactivecodes.in/