John Michailidis

In a time when Generative AI is reshaping markets, workflows, and even leadership expectations, senior decision-makers face a pressing challenge: how to cut through the hype and act with clarity. This 30-minute session creates space for critical reflection—not on what AI can do, but on what it should do for your organization.

We’ll unpack how foundational concepts like large language models, vector search, and knowledge augmentation are being applied in strategic contexts across industries. More importantly, we’ll address what true readiness looks like—organizationally, technically, and culturally. Through real-world examples and a pragmatic lens, you’ll be invited to assess both your company’s AI maturity and your own role in guiding its evolution.

If you’re navigating transformation or simply asking “what now?”, this session is designed to inform, challenge, and engage—not to sell, but to spark a more meaningful conversation.

Executive Learning Opportunity:
This discussion also introduces our executive-level Generative AI course: a focused 20-hour program built for C-suite leaders. The course provides the frameworks, language, and actionable roadmap needed to lead AI adoption with confidence. By linking strategic insights to hands-on decision-making, it ensures leaders don’t just understand AI—they can act on it.

AI is no longer optional — it’s a strategic imperative. Yet most organizations struggle to go beyond experimentation. In this session, we’ll move past the hype and focus on how to build a real-world, actionable AI adoption roadmap tailored to business needs, resources, and risk profile. Whether you’re in manufacturing, finance, healthcare, or services, the key to AI success lies not in chasing tools — but in aligning technology with your core processes, data maturity, and human workflows. We’ll explore the lifecycle of AI adoption through practical phases: business alignment, capability assessment, data infrastructure readiness, model integration, and continuous improvement. You’ll see how to transition from “isolated use cases” to full-fledged AI-enabled workflows that deliver measurable ROI. We’ll also discuss the most common traps companies fall into — from overinvesting in infrastructure to neglecting user trust and model feedback loops. Attendees will leave with a visual framework for decision-making, a maturity checklist, and key metrics of how to track progress. If you’re planning to adopt AI — or struggling to scale it — this session will give you the clarity and structure to move forward with confidence.

With a solid understanding of Generative AI, it’s time to explore how to effectively interact with and optimize AI assistants—without writing a single line of code. In this hands-on session, participants will engage with a custom-built Generative AI assistant powered by Llama, Llama, and DeepSeek-R1. Through guided, no-code exercises, attendees will experience firsthand how to refine AI outputs and maximize performance.

We’ll begin by experimenting with prompt engineering, where participants will explore different ways to structure inputs to guide AI responses. By adjusting tone, specificity, and constraints, they will see how small modifications can significantly improve relevance, coherence, and accuracy.

Next, we will dive into response optimization techniques, demonstrating how settings like temperature, response length, and system instructions impact AI-generated content. Attendees will practice adjusting these parameters through interactive exercises, gaining intuition on how to tailor AI behavior for different use cases.

Finally, we’ll discuss efficiency and best practices, showing how to make interactions smoother, reduce hallucinations, and achieve more reliable outputs. By the end of this session, participants will have a hands-on understanding of how to effectively work with GenAI models, empowering them to leverage AI assistants like a pro.

Generative AI is reshaping industries by enabling powerful applications in content generation, automation, and decision-making. However, to truly unlock its potential, one must understand not just how it works, but also how to optimize it for efficiency and accuracy. This session provides a structured introduction to Generative AI, making it accessible for novice students and AI enthusiasts.

We will start with the fundamental principles of Generative AI, explaining how models like Llama and DeepSeek-R1 generate human-like responses. Participants will gain insights into key architectural components, including transformers, attention mechanisms, and tokenization, as well as the differences between training and inference processes.

Moving beyond the basics, we will explore essential optimization techniques used to enhance model performance. This includes strategies for improving response accuracy, reducing latency, and balancing computational efficiency. Attendees will also learn about the latest advancements in fine-tuning methods, parameter-efficient training, and the trade-offs between different deployment strategies.

By the end of this session, participants will have a strong conceptual understanding of Generative AI, equipping them with the knowledge to build and optimize AI applications in the next hands-on session.

Join us for an exclusive workshop where you’ll learn how to build your own Generative AI (GenAI) application using Retrieval-Augmented Generation (RAG). In this hands-on session, we will guide you through creating an AI chatbot that retrieves relevant information from contextualized data. You’ll gain practical experience in storing vector embeddings in a vector database, a crucial component for efficient similarity searches in AI applications. Additionally, we’ll delve into the RAG architecture, exploring how integrating retrieval mechanisms with generative models enhances the accuracy and relevance of AI-generated responses.

This workshop is designed for anyone looking to dive deeper into building practical and impactful GenAI solutions. Don’t miss this opportunity to enhance your skills and bring your ideas to life!

Join us for an exclusive workshop where you’ll learn how to build your own Generative AI (GenAI) application using Retrieval-Augmented Generation (RAG). In this hands-on session, we will guide you through creating an AI chatbot that retrieves relevant information from contextualized data. You’ll gain practical experience in storing vector embeddings in a vector database, a crucial component for efficient similarity searches in AI applications. Additionally, we’ll delve into the RAG architecture, exploring how integrating retrieval mechanisms with generative models enhances the accuracy and relevance of AI-generated responses.

This workshop is designed for anyone looking to dive deeper into building practical and impactful GenAI solutions. Don’t miss this opportunity to enhance your skills and bring your ideas to life!