Using LLMs for Healthcare Predictions Through Fine-Tuning and RAG Techniques

A system that uses LLMs, fine-tuning and RAG techniques to make healthcare predictions.

A system that uses LLMs, fine-tuning and RAG techniques to make healthcare predictions. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on 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, LLM APIs, Python, and Hugging Face Transformers, with a clear project structure, setup instructions and sample data so that it can be run and extended.