AI Fundamentals

Most people use AI tools daily without a working model of what's actually happening underneath — which makes it hard to know when to trust an answer and when not to. This track builds that model from the ground up, in plain language: how these systems are trained, why they generate text the way they do, and why they can sound completely confident while being completely wrong.

We spend real time on failure modes specifically — hallucination, outdated information, bias in training data, and the gap between "sounds right" and "is right" — because that's the part most people never get taught, and it's the part that actually matters for using these tools responsibly.

No coding, no math prerequisites. This is built for someone who wants to understand the tool they're already using, not become an ML engineer.

• How it works: A plain-language model of training and generation, no jargon required.
• Where it fails: Hallucination, bias, and confident-but-wrong answers — and how to spot them.
• Prerequisites: None. No coding or math background needed.
• Format: Self-paced modules with short knowledge checks, not a lecture series.