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At Towards AI, we’ve built two free AI courses and are currently building more super useful ones, which you should subscribe to stay tuned or subscribe to my newsletter below, where I will surely announce all our projects. We recognized the bottleneck in the learning journey — those moments when you’re nestled deep in code or engrossed in a lesson, and questions bubble up. We knew the traditional approach of sifting through voluminous documentation or awaiting responses in community forums was not cutting it anymore. Even Googling is often hard for a very specific question, or you ask ChatGPT and get the usual knowledge cutoff answer or work with outdated documentation.
Enter the AI Tutor, engineered to be your companion, answering your queries in real-time with precision that mirrors an AI expert and professor. But how did we make this leap? That’s where the marvel of Retrieval Augmented Generation, or RAG, comes into play.
Now, back to our AI tutor. I mentioned retrieval augmented generation or RAG. RAG isn’t your everyday AI model or a copy of ChatGPT; it’s a big step forward in alignment and power for both the chatbot itself and the user, where the retrieval of information and its generation into coherent, precise answers coalesce seamlessly.
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FAQ
What is the open-source AI tutor?
It is a web application that answers AI and coding questions using a language model grounded in selected learning material.
Why use RAG for a tutor?
Retrieval supplies relevant course content before generation, making answers easier to ground and reference.
What happens when the knowledge base lacks an answer?
A responsible tutor should state the limitation rather than inventing information outside its approved material.
Can developers inspect or extend the project?
Yes. The open-source implementation lets builders study the pipeline and adapt its data or interface.
Does the tutor replace original learning resources?
No. It helps navigate and explain material, while source lessons and practice remain necessary for deeper learning.