Retrieval-Augmented Generation for Grounded Personalization in an AI-Powered Interview System

A retrieval-augmented generation approach for grounded personalisation in an AI-powered interview system.

A retrieval-augmented generation approach for grounded personalisation in an AI-powered interview system. 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, text or speech data preparation, model training, fine-tuning or prompting, evaluation with task-appropriate metrics, and a chat or web interface to use it. 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.