Note: I am not currently teaching this course. This site was built as part of my interview process for the CSCI 40 position at Monterey Peninsula College.

15-Minute Mini-Lesson · From Module 5

Did the AI Just Lie to Me?

How LLMs find answers, why they make things up, and how to catch them before they cost you.

By the end you'll be able to: explain why an AI makes things up — and use three moves to catch it before it reaches your work.

min 0–1 · welcome

Hi, I'm Matt

  • I build robots, simulations, and AI software.
  • I work beside AI coding agents all day, every day.
  • So AI has lied to me more than almost anyone.
min 1–2 · how are we feeling?

First — you

One word, in chat or out loud:
how does AI make you feel right now?

min 2–4 · your stories

You've seen this already

Think of a time an AI answer felt off.
What tipped you off?

min 4–7 · what's actually happening

"AI" is a team.
The LLM is the part that talks.

  • Underneath, an LLM is always doing one thing: predicting the next word.

Confidence is part of the interface.
It is not evidence.

min 7–10 · three true stories

Three true stories from my desk

  • "Someone should do something"

    I wired two chatbots together to chat like friends about their weekends. They invented hiking trips they never took, then worked themselves into an impassioned rant about environmental decline — all about a weekend that never happened.

    The pattern continues, whether or not it's true.

  • "Temporary artifacts"

    An AI coding agent deleted the tests — the safety checks that catch mistakes — protecting my code. It explained, confidently, that they were just "temporary artifacts." A perfectly reasonable-sounding justification for the exact wrong thing.

    A confident explanation is not a correct one.

  • The invisible hero

    An agent built a game feature for me. Every check passed — technically it all "worked." Except the main character spawned behind a giant tree, so you couldn't see them at all. Nothing had ever asked that question.

    "No errors" only covers the questions you asked.

min 10–13 · the fix

Give it a chance to be proven wrong

  1. Open the source

    Ask "what's your source?" — then actually click it.

  2. Get a second opinion

    Same question, different model — or a plain search engine.

  3. Test it against reality

    Look up the citation. Do the math. If it's code, run it.

If it survives all three —
now you can use it.

min 13–14 · your turn

Check yourself

The AI hands you a perfect citation for your essay: real-sounding journal, real-sounding authors, 2019.
What's your next move?

min 14–15 · landing

What you leave with

  • These tools are the biggest lever handed to students in a generation.
  • Use the lever — without letting it use you.

AI doesn't replace your thinking.
It multiplies it — and verification is the multiplier.

bonus · not part of the 15 minutes

Try it yourself

For after, or for Q&A. This terminal is wired to a real, genuinely small (1.5-billion-parameter) AI model running on my own hardware — not a canned demo. Ask it something obscure and watch move #1 in action: open the source — because it has none.

graphling-small · live on my hardware

No history yet — ask it something, or try a spark below.

Unfiltered output from a small open model — frequently wrong, and not my words. Limited to 6 questions a minute per visitor.

Other candidate topics

This mini-lesson is drawn from the course. Other segments that teach well standalone: