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AI productwarm-up
Explain how a large language model works to a non-technical executive.
How to approach it
Keep it to one idea: the model predicts the next small piece of text based on patterns from huge amounts of text. Then explain what that means for reliability.
How a strong answer opens
“At its core, the model predicts the next small piece of text, over and over, based on patterns learned from a huge amount of writing. That's why it sounds fluent, and also why it can be confidently wrong.”
Outline of a strong answer
Try answering out loud for a few minutes before you open this.
- The core idea: predict the next token (a word or part of one), over and over
- Trained on huge amounts of text, then tuned with examples and human feedback to follow instructions
- Why it can sound right but be wrong
- How products make it reliable: instructions, grounding in company data, checks
- What it costs to run and why speed varies
- Close with what it is good and bad at for the business
Where this question usually goes wrong
Going deep into neural network math with an executive, or overselling the model as something that knows facts and never errs.
Learn the method
How large language models work, for PMs in Module 7, AI product interviews.
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