LLMs Fundamentals

This course demystifies large language models for absolute beginners, anchored on one sticky metaphor — an LLM is "super-autocomplete" that predicts the next word at massive scale. It builds concepts in a deliberate order: what an LLM is, how prediction works, tokens, the three training steps (pre-training, fine-tuning, alignment), a brief history centered on the 2017 Transformer, and why LLMs exploded in popularity. It then covers real-world uses across every role, and devotes two full slides to the highest-stakes beginner material — limitations and hallucinations — reinforcing the golden rule that every output is a draft you must verify. Practical sections follow on tools, prompting basics, best practices, tips, common mistakes, and responsible use. It closes hands-on with two quizzes, four exercises (including one to deliberately catch a hallucination), a capstone project to automate one real work task, and a printable takeaways slide — with next steps pointing to your Prompt Engineering, MCP, and AI Agents courses.