Learn how to build a real AI Academic Tutor application from scratch using local AI models — without paying for LLM APIs.
In this hands-on course, you will build an AI-powered academic tutor that can understand student questions and provide helpful answers using a locally running Large Language Model (LLM).
You will learn how to use Ollama to run open-source AI models on your own computer and connect them to a Python application using FastAPI and LangChain.
You will also learn how to implement Retrieval-Augmented Generation (RAG) so your AI tutor can use academic documents and study materials as a knowledge source when answering questions.

What you will build
By the end of the course, you will have a working AI Academic Tutor application with:
A local LLM powered by Ollama
A Python/FastAPI backend
AI-powered question answering
RAG-based document understanding
Academic study material integration
A frontend connected to the AI backend
A complete end-to-end AI application running on your own computer
What makes this course different?

You don't need an OpenAI, Gemini, or other paid LLM API to follow this course. The AI model runs locally on your computer using Ollama, making it an excellent way to learn how modern AI applications work while keeping your development costs at zero.
You will learn by building a real application, rather than only learning AI concepts or making simple API calls.
By the end of the course, you'll understand how the different components of a modern AI application fit together and have a project that you can continue expanding with additional AI features.

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