Retrieval-Augmented Generation (RAG)-Based AI Teaching Assistant

A retrieval-augmented generation (RAG)-based AI teaching assistant.

A retrieval-augmented generation (RAG)-based AI teaching assistant. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want to work with language models and text or speech: preparing text data, using or fine-tuning a model, evaluating it and building an interface around it.

The main parts are retrieval over an indexed document collection that grounds the language model's answers, a conversational interface, text or speech data preparation, model training, fine-tuning or prompting, and evaluation with task-appropriate metrics. The reference build uses LangChain, FAISS, Python, Hugging Face Transformers, and spaCy, with a clear project structure, setup instructions and sample data so that it can be run and extended.