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AI Engineering · Prompting
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Think of a student getting ready for an exam. For weeks she works through the same five practice papers. By the end she scores 95 out of 100 on every one of them. Is she ready? Partly. Some of that 95 is real skill. Some of it is that she has seen these exact questions many times, and she remembers where the tricky ones are. The real exam will have new questions. Her score on it will probably be a little lower, and nobody can know how much lower until she sits it.
A good teacher knows this. She writes the real exam separately, and she does not let the student see it while the student practises. The practice papers are for learning. The unseen exam is for finding out what the student can really do.

Every lesson in this chapter so far has measured prompts on the same 40 customer messages from lesson 1. Those 40 messages are my practice papers. I wrote the prompts with them in mind, I read their mistakes, and I chose which prompt to call "the best" by looking at their scores. So this lesson does what the teacher does: it scores three of the chapter's prompts on 40 new messages that no prompt had ever been scored on, and compares the two results.