In today’s era of technological expansion, artificial intelligence (AI) and machine learning are integral parts of our daily lives, transforming the way we work, learn, and communicate. One of the tools that enables effective interaction with AI is the technique of prompting. It determines the topic or context of the response generated by the model.

Prompting involves providing language models with information in the form of an introduction or a question. The model analyzes this data and generates a response that is coherent with the given prompt. Properly choosing the prompt is crucial for the quality of the generated response, as different formulations can lead to diverse outcomes.

Let’s consider ChatGPT, developed by OpenAI, as an example. This model utilizes the technique of prompting for interactions with users. If you input “Tell me about the history of Egypt,” the model’s response may cover various aspects of that topic, from geography to history.

Now, let’s explore three main types of prompting: open-ended, closed-ended, and context-aware:

Open-ended Prompting

Closed Prompting

Contextual Prompting

In open-ended prompting, AI has full freedom to generate responses. In such a case, if we ask an AI model, “Tell me about the history of Egypt,” the model has complete freedom to respond – it can provide information in the form of narration, a list of facts, or a chronological description.

Closed-ended prompting is more specific. We provide the model with specific information or cues that influence the generated response. For example, if we ask the model, “Name five key events in the history of Egypt,” we expect to receive specific events in response.

Context-aware prompting takes into account previous interactions with the model, allowing it to better understand and adapt to the current context. For example, if we previously discussed ancient Egypt and then ask, “Who was the most important pharaoh?” the model understands that the question pertains to ancient Egypt.

The applications of prompting are numerous and diverse. It is used in many fields, such as content generation, question answering, translation, and more. When interacting with ChatGPT, we use prompting to guide the conversation in a direction of interest to us.

The concept of prompting also manifests in the everyday use of ChatGPT. The question we ask fundamentally shapes how the AI responds. When I ask, “Tell me about the latest astronomical discoveries,” ChatGPT delves into current trends and discoveries in the field of astronomy. However, if my question is more specific, such as, “Have any new exoplanets been discovered recently?” ChatGPT’s response will be more focused and precise.

Prompting is not limited to just asking a question and expecting a response. We can influence the style and form of AI’s answer by tailoring the prompt to our preferences. For example, if we ask, “Explain cosmic radiation as if you were explaining it to a five-year-old,” ChatGPT will adapt its response to a simple, child-friendly form.

It is not just the questions but also the manner in which we pose them that forms the basis for dynamic and engaging conversations with AI. In the case of ChatGPT, the context of previous questions and answers influences the ongoing conversation. For example, if we were discussing various music genres and then ask, “What about rock?” ChatGPT understands that the question pertains to rock as a music genre, not, for example, a geological formation.

The manifold applications of prompting make it a key element in the development and interaction with artificial intelligence. Through prompting, AI becomes not only more accessible but also more understandable and useful for each of us. Whether we want to generate content, answer questions, or translate text, prompting opens up new possibilities that previously seemed beyond our reach.