Thinking through real problems out loud
The way to make everything in lessons one to ten stick is to work through real problems and say the answers out loud. Good answers have a shape: a clear claim, the reason behind it, a concrete example, and then how you would know you were right. This lesson drills that shape until it happens without effort.
Answer an unfamiliar AI question in a structure that sounds considered rather than improvised.
Lessons 1 to 10. This lesson assumes the concepts and drills the delivery.
Plain-English mode
Each section opens with a short summary before the detail. Turn this off once the vocabulary feels familiar.
Claim, reason, example, measurement. Four moves. Almost nobody does the fourth, which is why it works.
Whether you are explaining a decision to a colleague, a sceptical stakeholder, or yourself at two in the morning, good answers have the same shape.
- 01The claim. What you would do, stated plainly and without hedging.
- 02The reason. Why, in terms of a constraint rather than a preference.
- 03The example. Something concrete, ideally from work you have actually done.
- 04The measurement. How you would know whether you were right.
The fourth move is the one almost nobody makes, and it is the one that changes how you are heard. Anybody can have an opinion about architecture. Saying how you would find out whether the opinion was correct is what makes it sound like judgement.
What follows are worked answers to the questions that keep coming up. Read them aloud, then try to rebuild each one from the four moves alone. The full set is part of the paid course.
Answer in your head first, then reveal. Getting it wrong is the part that makes it stick.
Which of the four moves is most often missing, and why does it matter so much?
The rest of this lesson
- When would you use retrieval rather than retraining?
- How would you know an AI tutor is any good?
- It cited the wrong country's law. Diagnose it.
- When would you deliberately not use AI?
- How do you handle data residency without killing your speed?
- It passes every test and users still do not trust it.
- How do you choose between a top model and a small one?
- Explain any of this to somebody non-technical
- Turning a vague priority into something measurable
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