Module 1: The AI Landscape (The Big Picture) What is AI? Differentiating between Narrow AI (Siri, Alexa) and Generative AI (ChatGPT, Claude). The "Brain" of AI: A non-technical intro to how models learn from patterns rather than following strict rules. Key Terminology: Training data, Inference, Hallucination, and Tokens. AI vs. Search Engines: Why AI gives "answers" while Google gives "links." Module 2: Mastering Generative AI (Prompt Engineering) The Anatomy of a Prompt: Context, Instruction, Input Data, and Output Indicator. Advanced Prompting Techniques: * Chain-of-Thought: Asking the AI to "think step-by-step." Few-Shot Prompting: Giving examples to guide the style. Multimodal AI: Learning to generate and edit images (DALL-E, Midjourney), audio, and video through text. Module 3: Data & Logic Foundations Data Literacy: Understanding that "Garbage In = Garbage Out"—how biased data creates biased AI. Basic Statistics for AI: Understanding probability (how AI predicts the next word). The AI Workflow: From identifying a problem to selecting a tool and refining the output. Module 4: Ethical & Responsible AI Hallucinations & Fact-Checking: Strategies to verify AI-generated content. Bias & Fairness: Recognizing how AI can reinforce stereotypes. Privacy & Security: What not to share with an AI (data leakage and corporate privacy). The Future of Work: How AI complements human creativity rather than replacing it. Module 5: Practical Hands-on Projects Personal Productivity: Using AI to summarize long meetings, draft emails, and manage schedules. Custom GPTs: Building a simple, "no-code" custom assistant for a specific task (e.g., a "Cooking Assistant" or "Study Tutor"). AI for Research: Using tools like Perplexity or Consensus to find credible sources. Recommended Free Resources for This Level: Google AI Essentials: A great certificate for workplace productivity. DeepLearning.AI - "AI For Everyone": The gold standard for non-technical beginners. Elements of AI: A free, interactive course from the University of Helsinki.