A neural network is not a search engine that finds a ready-made answer, and it is not a database that retrieves a fact from a table. It continues text in the most likely way based on what you have written. The practical consequence of this is direct. Your prompt is not a command that is executed, but the beginning of a text that is continued. Therefore, the more precisely you set the context, the narrower the space of possible continuations becomes, and the closer the result will be to what you need. This explains why a short prompt gives a general answer. Not because the system was lazy, but because a vague answer is the most likely continuation of a vague question.

What Really Works

The biggest difference is made not by clever wording, but by the amount of useful information in the prompt. Instead of asking to write a letter, it is worth specifying who it is addressed to, what your relationship is, what you want to achieve, what has already been discussed, and what tone you usually communicate in. Specify the length, format, audience, and what should not be done. The difference between asking to tell you about something and asking to explain it in three paragraphs for a person without technical training, without using terminology, is fundamental. If you want a certain style, show an example instead of describing it. Two or three examples of what the result should look like work better than a paragraph of adjectives, because the system recognizes a pattern well and interprets evaluative words poorly. When you insert text and ask it to work with it, the quality increases sharply, and the risk of fabrications decreases, because the system relies on what was provided rather than on its own memory. This is the most reliable way to get an accurate result. A long task with many conditions is performed worse than a sequence of short ones. First the structure, then the content of the sections, then editing, and at each step you correct the direction. The most common mistake is expecting a perfect result on the first attempt. Real work looks like a dialogue in which you say exactly what is wrong and ask to change a specific thing, rather than rewrite everything from scratch. The wording that it is better to acknowledge uncertainty than to invent really does reduce the number of invented details, although it does not eliminate them. This is one of the few techniques that has a theoretical basis, as I wrote about in the material about fabricated answers.

Now about the tips that move from one material to another and give almost nothing.

The wording that the system is a specialist with twenty years of experience has a minimal effect in modern models. A role does make some sense when it sets the audience or style of presentation, meaning that asking it to explain something as a teacher for first-year students really does change the presentation. But the assigned experience in years itself does not make the answer more accurate. Suggestions about tips, mentions of the importance of the task for your career, and similar techniques have been the subject of separate experiments, and they did not show a stable reproducible effect. This is folklore that spread faster than it was checked. Writing the word “necessarily” in capital letters does not make the requirement more significant. Clarity of wording works, emotional pressure does not. A paragraph about how you are now going to ask a very important question only adds noise. Get to the point. Collections of a hundred universal prompts give a mediocre result precisely because they are universal, meaning that they do not contain your context, which is the main ingredient.

Typical Mistakes

A question formulated as if the answer definitely exists forces the system to invent it. Asking to name three studies that confirm a claim almost guarantees three names even when such studies do not exist. Several different tasks in one prompt lead to some of them being performed superficially. The absence of a specified format produces a result in a form that you do not need, and then you have to redo it. Accepting the first answer without clarification means that you are using the tool with only a small part of its capabilities. Continuing a long dialogue when the conversation has gone in the wrong direction works worse than starting a new one with corrected wording, because previous unsuccessful attempts remain in the context and affect subsequent answers.

Practical Prompt Structure

There is no universal template, but a useful prompt usually contains several elements. A description of the situation and what the result is needed for. A specific task in one clear sentence.

Input data, if there are any, that is, text, numbers, or examples. Requirements for format, length, and audience. Constraints, meaning what should not be done. An example of the desired result, if you can provide one. It is not necessary to include everything every time, but the more important the task, the more elements are worth specifying.

What You Should Not Expect

Accurate facts from memory, that is, numbers, dates, quotations, and links, because this is exactly where most fabrications arise. Knowledge about events after the model's training was completed, if it does not have access to search.

Understanding of your specific situation without you describing it. A consistently identical result when repeating the same prompt. Ready-made material that can be published without verification, and this applies to any text for which you are responsible.

Summary

The entire technique of working with neural networks comes down to approximately three things, namely sufficient context, specific requirements, and a willingness to clarify the result instead of expecting a miracle on the first attempt. Everything else, including magical wording, assigning roles, and promises of rewards, is mostly an attempt to find a spell where ordinary skill in clearly setting a task is what works. This, by the way, is also the main reason why people who are good at writing technical assignments for colleagues almost immediately work well with neural networks.