AI & LLM Fundamental Course: definitions, the AI history timeline, types of AI, industry applications, the 3 pillars (Data/Compute/Algorithms), hype vs. reality myth-busting, key takeaways, and a 5-question quiz with answers.

Lesson Content covers: Tokens — what they are, why they matter, live demo guidance Training Data — scale comparisons, data sources, pre-training vs fine-tuning Neural Networks — the telephone/brain analogy, layers explained simply The Transformer — the spotlight analogy for attention, self-attention in action Inference & Parameters — temperature, top-p, max tokens, system prompts A 5-question quiz with answers

What is Prompt Engineering — why it matters and how much it impacts output quality Anatomy of a Prompt — the 6 components (Role, Context, Task, Format, Constraints, Examples) Zero-shot, One-shot & Few-shot — with real prompt examples in formatted code boxes Chain-of-Thought — side-by-side comparisons and zero-shot CoT trigger phrases System Prompts & Role Prompting — with a full customer service bot example The CLEAR Framework — before/after prompt makeovers