March 12, 2026 | University of Ioannina, Arta Branch
This intensive 2-hour lecture equips university students with both foundational knowledge and advanced expertise in Generative AI development using TypeScript. Blending essential theory with hands-on coding demonstrations, the session explores core principles and state-of-the-art techniques powering today’s production AI systems. Students will gain practical skills and deep insights to confidently build and innovate with Generative AI in real-world scenarios.
CRASH COURSE
2 HOURS
IN PERSON INSTRUCTOR LED TRAINING
HANDS ON FRAMEWORKS AND USE CASES
This session offers a comprehensive introduction to Generative AI development with TypeScript, designed for university students eager to bridge theory and practice. Participants will start by mastering TypeScript essentials tailored for AI projects, ensuring a strong technical foundation.
The lecture then demystifies how Generative AI and large language models (LLMs) operate, providing clear explanations of their underlying mechanisms. Through a live code demonstration, students will learn how to integrate LLMs into applications, including handling streaming responses for real-time interactivity. Finally, the session covers practical strategies for token management and cost estimation, empowering students to build efficient and scalable AI solutions.
This advanced session delves into the cutting-edge techniques and best practices for building intelligent AI agents. Participants will explore sophisticated prompt engineering methods, including Chain-of-Thought (CoT), ReAct, and Self-Ask, to enhance the reasoning and capabilities of large language models.
The lecture covers essential architectural patterns and design strategies for developing robust AI agents, followed by a live code demonstration showcasing the implementation of a ReAct agent equipped with practical tools.
The session concludes with a discussion of critical production considerations—addressing security, ethical implications, and reliability—to ensure responsible and effective deployment of AI systems in real-world environments.