AI Engineering: Build Production-Ready RAG Applications 2026

About this course

This Course is a great starting point for anyone who wants to learn AI Engineering, especially if you're interested in building RAG and LLM-powered applications.

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.

This course is designed to be practical, with every concept explained before implementing it in real code.

What you'll learn

Understand what Large Language Models (LLMs) are and how they work.

Learn about tokens, context windows, temperature, and hallucinations.

Understand the architecture behind Retrieval-Augmented Generation (RAG).

Understand the fundamentals of AI Engineering and how LLM-powered applications are

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