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Artificial Intelligence (AI)
Module 1: Introduction to AI (Basics, Learning, and Responsible Use)
This module introduces the fundamentals: the difference between AI and traditional software, how machine learning works (supervised, unsupervised, and reinforcement learning), and why most modern AI is task-focused rather than truly human-like. You’ll also build core AI literacy by learning common terms (like models, training vs. inference, tokens, and automation), what today’s popular AI tools can and can’t do, and when you should be cautious about accuracy. We’ll connect this to responsible use by discussing bias, privacy, and how to spot red flags that signal you should verify an output.
Curious about Artificial Intelligence but not sure where to start? This workshop is designed for everyday learners who want a simple, clear understanding of what AI is, how it works, and why it matters—no technical background needed.
By the end of this workshop, you’ll walk away with a clear understanding of how AI works, where it’s going, and how it might fit into your world—whether for work, school, or just staying informed.
Module 2: Prompt Engineering, RAG, and Generative AI (Text + Images)
This module focuses on using AI effectively and getting better outputs. You’ll practice prompt engineering strategies (from simple prompts to more structured methods) and learn how RAG (Retrieval-Augmented Generation) helps ground answers in reliable sources to reduce hallucinations. We’ll also preview how generative AI creates both text and images, including why transformers and diffusion models are used for different kinds of creative output.
Module 3: Agentic AI (Frameworks, Risks, and Verification)
This module looks at what’s next with Agentic AI—systems that can plan steps, remember context, use tools, and complete multi-step workflows rather than only responding to one prompt at a time. You’ll learn why this is powerful, but also why it introduces risks like loops, runaway tool costs, and unintended actions. We’ll finish with practical safeguards and verification patterns—human checkpoints, grounding, and review techniques—so agent-style systems stay useful and trustworthy.
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