Using Effective Prompts for AI

Better prompts reduce ambiguity, save time and lead to better decisions. They do not affect how the AI feels about anything, because it has nothing to feel — and treating it as a colleague to be pleased rather than a tool to be directed tends to make people vague out of politeness.

A prompt is simply the input you give an AI to get the output you want — the instruction, the context and any examples. Treating it as an instruction you can refine, rather than as a search box, is what separates useful results from disappointing ones.

Open questions give the model room to explain, compare and expand — the same reason they work with people. Closed questions get you a yes or no, leading questions hand back your own assumption, and cramming several topics into one prompt gets you a shallow answer to all of them.

Be specific, give context, and include examples. Specificity says what you want, context says who it is for and what situation you are in, and an example shows the form of the answer — which is usually quicker than trying to describe that form in words.

AI took shape as a field in the 1950s — the 1956 Dartmouth workshop is usually treated as the starting point, and Alan Turing's work earlier that decade framed the questions it set out to answer. What has changed recently is not the idea but the data and computing power behind it.