IS&T RCS Tutorial - Applied Generative AI: Building LLM-Driven Chatbots and RAG Systems, Part One (Hands‐on)

Note that this is Part One of a two-part tutorial and you should also register for Part Two. What to Expect: In this hands-on tutorial, participants will gain practical experience building LLM-driven chatbots and Retrieval-Augmented Generation (RAG) systems using Python. The session will introduce core concepts such as large language models, with a clear focus on why and when RAG is needed for real-world applications. Through guided exercises, attendees will implement end-to-end RAG pipelines that enable chatbots to answer questions directly from documents. The emphasis is on applied system design, and experimentation rather than training models from scratch. Key Topics Covered Introduction to LLMs Introduction to RAG (Retrieval-Augmented Generation) Why RAG is needed? How retrieval works: embeddings -> vector databases -> similarity search Hands-on practice: Build RAG chatbots that answers questions from documents Prerequisites: Intermediate to advanced Python programming proficiency is required. You should be comfortable working with Python packages, virtual environments, and reading and modifying existing Python scripts. Prior exposure to NLP concepts (tokens, embeddings, transformers) is helpful. A basic understanding of machine learning and deep learning concepts with some practical experience. Recommended Background: If you are new to machine learning, consider attending the preceding tutorials on Machine Learning with Python (Scikit-Learn), Deep Learning with PyTorch, and Open Source Generative AI with Hugging Face to build a solid foundation. Who This Tutorial Is For: Researchers, engineers, and data scientists looking to apply LLMs to real document-based tasks. Practitioners interested in building RAG-based chatbots for knowledge bases, documentation, or reports. Participants with diverse backgrounds (computer science, data science, applied research, domain sciences) who want a practical entry point into Generative AI. Who This Tutorial Is Not For: Those seeking a deep dive into training LLMs from scratch or into the mathematical foundation of NLP or LLMs or optimization theory Participants with no prior Python programming experience. Get ready to explore LLMs, experiment with open-source models, and build your own AI-powered ChatBot!