In this video, Carter Rabasa from DataStax demonstrates how to build a production-ready AI chatbot using Retrieval Augmented Generation (RAG) with Langflow, OpenAI, and Azure. The tutorial covers creating a RAG application by ingesting data into a vector database, using Langflow to build AI flows, and deploying the application as a Python Flask app on Microsoft Azure. Carter explains the importance of RAG for integrating private data with LLMs and shows how to use Langflow’s visual editor to construct the chatbot’s logic. The demonstration includes setting up a vector database with AstraDB, embedding data, performing vector searches, and constructing prompts for accurate responses. The video also highlights the use of Langsmith for observability and monitoring LLM calls. Carter emphasizes that these tools make AI development accessible to developers without extensive machine learning expertise.