AI Engineering: Build Production-Ready RAG Applications 2026

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 learnUnderstand 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 built. Build the complete Document Processing Pipeline. Build AI systems that can search millions of document chunks in seconds.

Expose your RAG system through production-ready REST APIs. Build the complete Question Answering Pipeline. Load PDF, DOCX, TXT, and Markdown files.

Split documents into chunks for efficient retrieval. Generate embeddings and store them in ChromaDB. Retrieve relevant information using semantic search.

Create prompts that combine retrieved context with user questions. Learn practical Prompt Engineering techniques for RAG applications. Build a clean, scalable RAG project structure.

Develop REST APIs using FastAPI. Test APIs with Postman. Connect your application to modern LLM providers.

Build an interactive AI interface with Gradio. Deploy your RAG application to Render. Compare different LLM families and learn how to choose the best model for your RAG applications.

Evaluate RAG systems and measure both retrieval and generation quality. Learn how to improve retrieval using techniques such as query enhancement, metadata filtering, hybrid search, multi-query retrieval, and reranking. Learn how to handle errors and failures in AI and RAG applications.

Build more reliable and production-ready AI applications. Understand how the different components of an AI application work together, from user input and retrieval to LLM generation and evaluation. Who this course is forPython developers who want to build AI-powered applications.

Backend developers interested in Generative AI and LLM applications. Software engineers who want to get started with AI Engineering. AI and Machine Learning engineers who want to learn practical RAG development.

Developers who want to build production-ready applications powered by LLMs. Software engineers who want to understand RAG from scratch and learn how the components work together. Developers interested in Prompt Engineering, retrieval systems, and LLM application development.

Students who prefer learning by building real-world projects. Anyone interested in modern LLM and Generative AI application development. Anyone looking for a practical starting point for learning AI Engineering through a complete RAG project.

RequirementsBasic knowledge of Python. No prior RAG or AI experience is required. A computer running Windows, macOS, or Linux.

By the end of the course, you won't just know what Retrieval-Augmented Generation (RAG) means. You'll understand how a complete RAG system works from document ingestion and processing, through retrieval and prompt construction, all the way to the final generated answer. You'll build a complete, production-style RAG application using:Python + ChromaDB + FastAPI + Gradio + Modern LLMsAlong the way, you'll learn the practical foundations of AI Engineering and LLM application development, including:Building document processing and retrieval pipelines.

Working with embeddings and vector databases. Writing effective prompts for RAG applications. Working with different LLM providers and choosing the right model for your use case.

Evaluating both retrieval quality and generated answers. Improving retrieval performance with practical retrieval enhancement techniques. Handling errors and building more reliable AI applications.

Exposing your AI application through REST APIs. Building an interactive interface for your RAG system. Deploying your application to the cloud.

Most importantly, you'll gain a practical foundation in AI Engineering that you can build on to create your own LLM-powered applications. You'll be ready to take the concepts and project further into real-world use cases such as company knowledge bases, document assistants, customer support systems, internal search, knowledge assistants, and other AI-powered applications.