Mosaic AI

I built a 54-minute hands-on RAG tutorial on Databricks — from PDF loading to retrieval and LLM answers

Hi Everyone I recently published a hands-on tutorial where I build a basic **RAG pipeline on Databricks** from scratch. The goal of the video is not just to use a high-level RAG framework, but to show what actually happens behind the scenes. In the video, I cover: * Loading PDF files inside Databricks * Extracting text from PDF pages * Splitting documents into chunks * Creating embeddings using Databricks embedding endpoints * Building a simple manual retrieval system using vector similarity * Creating prompts from retrieved chunks * Generating grounded answers using Databricks LLM endpoints * Using `databricks-langchain` for embeddings and chat models I intentionally kept the implementation simple so that beginners can understand the core mechanics of RAG before moving to more production-level tools like Vector Search, Unity Catalog, MLflow, etc. Here is the video: [https://youtu.be/7QY1iXPLgRg](https://youtu.be/7QY1iXPLgRg) Would love to hear feedback from people working with Databricks, RAG, LangChain, or enterprise GenAI systems. Also curious: for production RAG on Databricks, would you prefer starting with a simple manual implementation like this first, or directly using Mosaic AI Vector Search / Databricks Vector Search from the beginning?