Building a Production RAG System | Retrieval-Augmented Generation Explained for Developers

In this video, I explain Retrieval-Augmented Generation (RAG) from fundamentals to a production-ready system architecture. This session is designed for developers, data engineers, AI engineers, and tech leads who want to understand how modern LLM applications use private knowledge safely and accurately. 🔍 What You Will Learn • What is RAG and why LLMs hallucinate • How RAG solves stale knowledge problems • Embeddings and Vector Databases explained • RAG Ingest Pipeline (Load → Split → Embed → Store) • Query Pipeline (Retrieve → Prompt → Generate) • Real-world enterprise use cases • Advanced RAG patterns: • Agentic RAG • Multi-modal RAG • GraphRAG 💻 Hands-On Code Included This video demonstrates a working RAG pipeline using: • Python • LangChain • Chroma Vector Database • OpenAI Embeddings You will see how documents are chunked, embedded, stored, and retrieved to generate accurate answers grounded in real data. 🏢 Real Enterprise Applications RAG is widely used in: • Customer support chatbots • Enterprise knowledge search • Legal and compliance systems • Healthcare assistants • Sales and RFP automation • Education and tutoring platforms 🎯 Who This Video Is For • Software Developers • Data Engineers • AI Engineers • Tech Leads • Anyone building LLM-powered systems 📦 Tools & Technologies Python | LangChain | ChromaDB | Vector Databases | OpenAI | Embeddings | LLMs #AI #RAG #LangChain #VectorDatabase #LLM #GenerativeAI #MachineLearning #AIEngineering #Python #semanticsearch https://medium.com/nextgenllm/how-retrieval-augmented-generation-rag-works-end-to-end-architecture-guide-e4e6ad72ef52?postPublishedType=initial