New guide introduces LLMs for scientists and professionals
Written for a master’s course in AI in medicine at the University of Turin, Italy, the document covers core concepts like tokenization, embeddings, training, and prompting—without deep math. It highlights key limitations: hallucinations, bias, reproducibility, and the need for human oversight when using LLM outputs.
Key points
- Guide targets scientists and professionals, avoiding technical math while covering core LLM concepts and risks
- Focuses on limitations like hallucinations, bias, and the need for human verification of outputs
- Includes a practical exercise for running a local LLM with **Ollama** and **Open WebUI** using scientific papers
The guide also addresses privacy, governance, and security. Advanced topics include Retrieval-Augmented Generation (RAG), vector search, and practical workflows. A hands-on exercise walks readers through running a local LLM with Ollama and Open WebUI, emphasizing evidence-based reasoning over raw plausibility. No coding experience is required.
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