04-19, 09:00–10:30 (US/Eastern), Room 120
This workshop will provide a comprehensive introduction to Large Language Models (LLMs), covering their capabilities, structure, and practical applications. Participants will learn prompt engineering techniques, retrieval-augmented generation (RAG), agentic AI design, fine-tuning strategies, and model evaluation methods. The session will conclude with a discussion on the future of AI-powered reasoning machines.
The rapid evolution of AI and Large Language Models (LLMs) has opened new possibilities for automation, content generation, and interactive agents. This hands-on workshop is designed for developers, researchers, and AI enthusiasts who want to deepen their understanding of LLMs and learn how to harness their full potential. Topics covered include:
- How LLMs work and the role of reinforcement learning in training
- The art and science of prompt engineering, including zero-shot and few-shot techniques
- Retrieval-Augmented Generation (RAG) for integrating external knowledge
- Agentic AI: Designing chatbots and workflow agents
- Fine-tuning models using LoRA for custom behaviors
- Evaluation methods for improving AI performance
- Future trends, including multimodal models and new interaction paradigms
Attendees will leave with practical skills, implementation strategies, and insights into the future of AI-powered applications.
No previous knowledge expected
John Berryman is the founder and principal consultant of Arcturus Labs, where he specializes in AI application development (Agency and RAG). As an early engineer on GitHub Copilot, John contributed to the development of its completions and chat functionalities, working at the forefront of AI-assisted coding tools. John is coauthor of "Prompt Engineering for LLMs" (O'Reilly).
Before his work on Copilot, John's focus was search technology. His diverse experience includes helping to develop next-generation search system for the US Patent Office, building search and recommendations for Eventbrite, and contributing to GitHub's code search infrastructure. John is also coauthor of Relevant Search (Manning), a book that distills his expertise in the field.
John's unique background, spanning both cutting-edge AI applications and foundational search technologies, positions him at the forefront of innovation in LLM applications and information retrieval.