Build a Production-Ready RAG System with Python & OpenAI

About this course

Build your own fully working AI system that can read your documents and answer questions with accuracy.In this step-by-step, project-based course, you will learn how to use Retrieval-Augmented Generation (RAG) to overcome the limitations of traditional AI models. Instead of relying solely on the model’s internal memory, you will connect GPT to your own knowledge sources such as PDFs, policies, reports, and business documentation.You will learn the complete pipeline: document ingestion, chunking, embeddings, vector search, and contextual answer generation. All of this will be combined into a clean, user-friendly Streamlit application that you can run locally or deploy to the cloud.Throughout the course, you will gain hands-on skills in Python, the OpenAI API, semantic search, creating embeddings, designing a chat interface, and deploying applications online.By the end of the course, you will have built and shipped a working RAG system that you can personalize, extend, and showcase in your portfolio. Whether your goal is automating customer support, improving document access, or creating new AI-powered products, this project provides a strong foundation for building real-world AI solutions.This course is accessible to beginners, while still offering depth for intermediate learners who want to advance their AI engineering skills.