Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 1st Apr 2026, 02:46:17pm CEST
|
Agenda Overview |
| Session | ||
TT 4a (1/2) - From Prompts to AI Applications: A Hands-On Introduction to RAG and LLM Systems
| ||
| Session Abstract | ||
|
Brief Description and Outline: This 4-hour hands-on tutorial introduces practical methods for building Generative AI (Gen AI) applications using prompt engineering and Retrieval-Augmented Generation (RAG). The session moves from controlling large language model (LLM) behavior to grounding models in external documents and assembling a simple AI system using Python. - The tutorial includes three modules: ● Module 1 (60 minutes): Prompt Engineering Fundamentals. ● Module 2 (90 minutes): Retrieval-Augmented Generation (RAG) and LangChain. ● Module 3 (90 minutes): Assembling a Simple AI Application. - Goals: This tutorial equips participants with practical skills for building reliable AI-powered applications. By the end of the session, participants will be able to: ● Design structured and controllable prompts ● Explain and implement a basic RAG pipeline ● Connect language models to external documents ● Assemble a minimal AI application using Python –> The tutorial supports HAICON26’s focus on applied AI by bridging theoretical understanding and real-world implementation. As Gen AI becomes central to research workflows, grounding models in real data and ensuring output reliability is increasingly important. - Presenters Experience: Dr. Osama Hamed is a postdoctoral researcher specializing in applied artificial intelligence and machine learning. He has several years of experience working in the fields of natural language processing (NLP) and AI. Dr. Hamed earned his PhD in NLP from the University of Duisburg–Essen in 2019. Additionally, he accumulated several years of teaching experience as both an assistant professor and while holding a master’s degree. Eng. Fatima Rajab is a Computer Systems Engineering graduate and QA Automation Engineer with a strong focus on Gen AI and RAG. She has experience integrating AI techniques into software testing workflows to improve reliability and traceability. - Target Audience: Researchers, developers, data scientists, and graduate students interested in building practical GenAI applications. Familiarity with Python programming is required. No prior experience with LLMs, RAG or LangChain is necessary. The tutorial begins with foundational concepts. Participants should bring their own laptops. - Keywords: Prompt Engineering, LLMs, RAG, LangChain, Gen AI |
