One idea, five lenses

By the end of this chapter you will have one idea of your own, explained five ways by a model, and you will have seen the lens working rather than the magic. It takes an hour, any chat tool will do, and the exercise is the one the article behind Lenses, roles and loops asked the editor to plan for this part: one idea, your own choice, explained for a teenager, a musician, a doctor, a football fan, and in another language.

Why this works

From a model's point of view, translating from one language to another is no different from translating for a particular topic, region, audience or persona. Explain this to me as if I were a teenager. Explain it as if I were a musician. Explain it with medical references. Explain it in football terms. The model can do all of that because it has the frame of reference: it holds all those worlds inside it, and the prompt puts the right lens in front of the idea. The analogy is the reason someone understands something.

It is the same with people who say they are not good at maths. Watch them in their day-to-day and they do a huge amount of maths. They are just not good at the kind of maths we decided was maths: sums, calculus, trigonometry, algebra. Mathematics is much more than that. The lens was wrong, not the person. That is why this exercise matters more than it looks: it shows you, on an idea you already understand, that the explanation is a choice, and that the choice is yours.

That matters twice over for the two readers of this book. If you are starting out, it shows you that an explanation you did not follow was a lens that did not fit, not a limit of yours. If you have spent twenty years inside a field and are starting a company from what you know, it is the skill you will use every day: the thing you understand, explained to the customer, the investor and the agent, each in their own terms, without losing the thing.

What generative AI is said where the power comes from. A language model predicts the next word, but it predicts it from an incredibly rich picture of how things connect, and your prompt narrows that picture down. The better your prompt, the better the output, because the model knows better which concepts you are talking about. The five lenses are five different narrowings of the same picture.

The exercise

1. Pick the idea. Something you understand well enough to know when an explanation of it is wrong. That is the whole test, so do not pick something you are still learning. If your field is your expertise, pick the idea in it that outsiders most often get wrong.
2. Write your own version first. Two or three sentences, plainly, before you ask the model anything. You will need them in step 5.
3. Ask five times, in five separate sessions. One lens per session, so the answers do not borrow from each other: a thread is the model's frame of reference, and everything in it is context for the next answer. Explain it to a teenager. Explain it to a musician. Explain it with medical references. Explain it in football terms. Explain it in another language, one you can read.
4. Read the five side by side. Look for what stayed the same: that is the idea. Look for what changed: that is the lens. Then look for the analogy each one reached for, because the analogy is doing the explaining.
5. Check them against your version. Where a lens says something your version did not, ask whether that is the idea seen from a new side, or the lens filling a gap. The doctor's version may name a condition; the football version may name a tactic. Look them up.

Step five is where the exercise earns its place. In a talk in Lisbon in June 2024 I said that hallucinations "Are a FEATURE when creating content and when we want creativity, But are a BUG and PROBLEM when we want facts, evident and trust", and I still think so. Inside the model just about everything is connected to everything, and it is hard for the model to know where the lines are. The text you give it acts as a short-term memory of which connections actually hold here. A lens is permission to reach for connections, which is exactly what makes a good analogy and exactly what makes a confident wrong detail. The medical references are the ones to check.

Then ask to be corrected

There is a sixth step, and it is the habit I most want you to take from this chapter. When I recorded the memo behind What generative AI is, I asked the model helping me with this book to correct me where I was wrong, and it did: classic machine learning learns too; inside a model there are vectors, not a stored graph, so my graph is a metaphor; the prompt does not change the model, it conditions what the model computes; most image models remove noise step by step rather than predicting the next piece. Those corrections are in the book because they are the kind of thing a reader should learn to ask for.

So, in a sixth session, paste your own two or three sentences from step 2 and ask where they are wrong, and what the model would check. Then check it. Do the same with the lens you liked most. The asking is yours to do, and so is the checking; that is what You are lucky to be here meant by thinking with it, not delegating your thinking to it.

Once you have done this on one idea, you will not see a chat answer the same way again. You will see the lens, and the picture behind it being narrowed, and you will know that you can be the one who sets the constraints. It stops being magic. That is the point of understanding it.