AI & LLM foundations
What AI, machine learning, and large language models actually are, how they work at a topline level, and where their capabilities end.
Every executive now has an opinion about AI. Most of those opinions are built on hype, headlines, and a demo someone saw once. This block fixes that. You will finally understand what an AI actually is, how a large language model works under the hood, and why that matters for every bet you are about to place with your budget and your reputation.
We start with the mechanics. What is AI, what is machine learning, and what is a large language model, explained without the academic fog. Then we walk through what happens the moment you hit enter: tokens, training, and inference. You will learn why the context window is the model's working memory, and why that single concept explains half the frustration your teams have with these tools.
From there we get honest about power and limits. You will see what LLMs are genuinely great at, and where they fall flat on their face. We tackle hallucinations head on, because a confident wrong answer is far more dangerous than an obvious one, and your organization is already acting on those answers. Finally, we strip away the myth of machine understanding and show you what a model truly does: prediction, not comprehension.
Why does a senior leader need this? Because you cannot govern, fund, or challenge what you do not understand. When you know how the machine thinks, you ask sharper questions, you spot vendor nonsense in seconds, and you set policy that survives contact with reality. This is not a coding course. It is the mental model that lets you lead the AI conversation instead of nodding along in it.
What you'll master
- Explain the difference between AI, machine learning, and large language models in plain business language
- Trace what happens from prompt to answer, including tokens, training, and inference
- Use the context window concept to diagnose why AI tools succeed or fail on your tasks
- Judge where an LLM adds real value and where it becomes a liability
- Detect and mitigate hallucinations before they reach a decision or a customer
- Challenge vendor claims by understanding that models predict rather than understand
- Set informed expectations for teams deploying AI across the business