Is RAG Dead in 2026? - Build Local RAG from First Principles

Learn to build a Retrieval-Augmented Generation (RAG) system from scratch using Python and first principles in this 14-minute tutorial. Explore why RAG remains essential even with large context windows exceeding 1 million tokens, and discover the fundamental concepts behind retrieval-augmented generation without relying on complex frameworks or vector databases. Build a local financial analyst agent using Ollama and Gemma 3, implementing TF-IDF for document retrieval to understand the mathematical foundations of the technology. Set up a complete RAG pipeline from the ground up, see the system in action through a practical demonstration, and understand the limitations of simple RAG implementations in production environments. Gain insights into the core mechanics of RAG systems while working with local models and learning why this approach continues to be valuable in modern AI applications.