Break the Spell of Prompt Magic

Now that large language models have become popular, a whole crowd of people has appeared promoting what they call “prompt engineering.” They treat prompts as something mysterious and powerful, producing long, cumbersome templates and calling them “prompt secrets,” even selling them as if they were some kind of rare professional skill. In reality this completely misses the point. What are we actually using AI for? Essentially we are using its computing power and knowledge base to assist our thinking. If most of our effort goes into figuring out how to recite a few magic phrases, then we have simply fallen into a new form of superstition.

In my view, using large models tends to fall into a few different stages. The first, and the most straightforward one, is when you already have your own ideas, logic, and substance. You know clearly what you want to express, and the structure of your argument already exists in your mind. In that situation AI becomes a very useful assistant. You might ask it to help with brainstorming, to see whether there are blind spots you have not considered, or to polish a paragraph so that it reads more smoothly and persuasively. In cases like this you do not need any complicated prompt techniques. You simply talk to it the way you would talk to an intelligent person, stating your ideas directly. Because the initiative remains in your hands, your own thinking becomes the best prompt.

The second situation is when you genuinely do not yet have a clear idea, and perhaps cannot even describe the problem well. In that case prompt techniques can be somewhat useful, but many people are applying them in the wrong way. What they try to do is design a complicated prompt so that the AI can skip the thinking process and immediately produce the final answer. That approach rarely works well. A better method is to use prompts to discipline your own thinking. Through several rounds of dialogue with the AI you gradually break down the confusion, clarify the logic, and eventually see the question more clearly until your own viewpoint emerges. In other words, the process is a form of intellectual training. It turns the second situation into the first, rather than simply chasing after an answer.

The third situation lies somewhere between the two. It is a dynamic process in which you move back and forth between having partial ideas and gradually refining them. But regardless of which situation you are in, the core factor remains your own mind. If someone has no substance or logic of their own, then even with the most perfect prompt template in the world, the output from the model will still be nothing more than empty and lifeless text. We should therefore be cautious about the tendency to turn prompting into a kind of technical ritual. If a day ever comes when communicating with AI requires mastering a complicated and almost mystical technique, that would not mean AI has evolved. It would mean human expressive ability has declined. The people who are truly good at prompting are usually those with the clearest logic and the deepest understanding, and they often do not rely on any special tricks at all.

This demystification of technique is closely related to the qualitative shift that has taken place in the underlying logic of modern models. If we compare AI to a car, the models of two years ago were like early manual racing cars. You had to manage every gear and clutch precisely through prompt techniques just to make them run properly. Today’s top models are closer to intelligent vehicles with advanced autonomous driving. They no longer require complicated operational instructions. What they need from you is a clear destination and ongoing feedback about how the journey feels. If you still find yourself memorizing those supposedly magical prompt formulas, the problem may not be your skill but simply that the car you are driving is outdated.

For that reason it is usually more productive to invest in the best models available rather than spending endless time refining prompts on mediocre ones. This is not merely consumption. It is an investment in cognitive leverage. When people obsess over prompts on weak models, they are essentially adapting themselves to the limitations of the machine. When they converse directly with the most capable models, the situation reverses and the machine begins to serve human insight. Once the tool becomes intelligent enough, all the elaborate “engineering” naturally collapses back into ordinary language and clear reasoning. You simply speak to the system as you would to a highly intelligent collaborator, honestly sharing your insights or even your confusion, and it will be able to carry the conversation forward.