The key to effectively using generative AI lies in “how to ask.” Generative AI is not a simple command-processing tool. It is a probability-based language model that analyzes input sentences to predict the next word. Therefore, the quality of the results can vary greatly depending on the content, structure, and context of the questions. This is why some receive clear answers while others are puzzled by unexpected results.
The longer and more specific the question, the better. Many users ask questions of generative AI as if giving commands, such as “Summarize this,” “Translate this,” and “Write a report.” These instructions are mostly ambiguous because AI can’t fully understand human intent. Questions lacking context, subject, or purpose tend to yield unclear responses.
To achieve clearer results, sentences should be structured. If you include key information, like the subject, purpose, length, format, and target, in your request, AI can generate more specific and consistent responses. For example, you could say, “Summarize the recent trends in the domestic electric vehicle market into a 3-minute presentation for a college student.”
If you specify the purpose of the information, AI can appropriately adjust the style and level of explanation. For a youth audience, it will use simple words and short sentences, while for an expert audience, it will enhance technical terms and statistics. It is as if you are designing the desired outcome in advance to achieve it.
When requesting multiple tasks simultaneously, AI may get confused. If you give compounded instructions like “Summarize it and create a table,” it may not know which to process first. In such cases, divide the request into steps and proceed by confirming each step.
If you are dissatisfied with the first response, instead of changing the entire sentence immediately, it is more effective to request partial modifications based on the previous response. For example, you could refine the output gradually through follow-up questions like “Summarize it more simply” or “Reduce the technical jargon.”
Generative AI also has the function of remembering dialogue and maintaining continuity. By utilizing this, lengthy documents or complex tasks can be efficiently handled by breaking them down sequentially.
Including examples in your questions can improve the response quality. If you say, “Write it in this style” or “Organize it like the example below,” AI can more accurately grasp the form of the output you expect. Since AI is a model trained on previous data, its consistency and accuracy are enhanced when it can refer to similar formats or sentences.
Examples assist the model’s understanding of sentence structure, vocabulary, length, and composition method. In particular, in tasks such as creative document writing, planning, or content creation, the presence or absence of examples can determine the quality of the outcome.
The skill of questioning is the new literacy. In the past, information acquisition required the ability to “find.” Now, the ability to “ask” is more important. To get the information you want, you must ask the right questions. Generative AI creates outputs according to the user’s request, but the standard lies entirely in the input sentence.
Questioning technique is not a mere skill, but an extension of literacy. Literacy refers to the ability to read, understand, and utilize text. Now the ability to request structured information, especially writing skills, has become important.
There is only one thing to remember to effectively use generative AI: the accuracy of the question brings forth an accurate answer. If the user’s question is vague, AI will only provide a correspondingly vague answer.
Technology continues to advance, but it is still up to humans to handle it. Generative AI is no exception to this rule.