Large language models can write, summarise, and reason — but most people use them with no idea of what is happening under the hood. That gap is exactly why prompts misfire and why a confident answer can be quietly wrong.
This course opens the black box without a single equation. You will see how an LLM builds an answer one token at a time, why training on huge amounts of text makes it fluent but not infallible, and how simple controls like temperature, system prompts, retrieval, and fine-tuning let you shape what it does. Practical intuition you can use in any AI tool today.