Imagine building an AI assistant that answers questions using your own PDFs, documents, or company knowledge—not just what an LLM learned during training. That’s exactly what Retrieval-Augmented Generation (RAG) enables. In this course, you’ll build a complete production-ready RAG application from scratch using Python, FastAPI, ChromaDB, Gradio, and modern Large Language Models including OpenAI, Gemini, Groq, and open-source models.
Retrieval-Augmented Generation (RAG) has become one of the most important techniques for building AI applications that can answer questions using your own documents. In this course, you’ll learn how RAG works from the ground up and build a complete RAG application step by step using Python, FastAPI, ChromaDB, Gradio, and modern Large Language Models (LLMs).
We’ll start by understanding the core AI concepts behind RAG, including LLMs, tokens, context windows, embeddings, and the two pipelines that power every RAG system. Then we’ll build each part of the application in code, organize the project using a scalable folder structure, expose it through a FastAPI backend, create a simple web interface with Gradio, and finally deploy the application so it’s ready to use.
