Understand the basics of AI: Prompt, Model, Generative AI

Understanding the fundamental concepts associated with artificial intelligence is crucial for effectively utilizing these technologies. In this context, we will address key terms such as "Prompt", "Model", and "Generative AI". The explanation will be provided in clear and simple language so that you can immediately get an overview. Additionally, you will experience a live demo in ChatGPT and Midjourney that will show you how to apply these terms practically.

Main Insights

• A prompt is the text you enter into an AI tool to receive a response.
• A model is a trained AI system that has learned to perform specific tasks.
• Generative AI creates new content such as text, images, and music based on your prompts.

Importance of a Prompt

The term "prompt" is crucial when working with artificial intelligence. Imagine you are in a restaurant and give your order to the server. In this case, your prompt is the order you communicate to the AI. The definition is simple: A prompt is the text you enter to receive any kind of response or output from the AI.

For example, if you request an Instagram post about home office tips in ChatGPT, that is your prompt. The clearer and more precise you formulate it, the better the result will be. A general prompt like "home office" is far too vague, while a specific prompt like "Write a casual Instagram post with five tips for ergonomic working in the home office" is much more effective.

The Model

Now let's move on to the next term: the model. A model is the heart of AI and serves as the engine that processes your prompts. It is a trained AI system that has been fed large amounts of data — including books, websites, and many other sources.

The functionality is determined by parameters and an interface. The parameters are the billions of values that the model learns during training. When you enter your prompt, the model uses these parameters to generate an appropriate output. For example, ChatGPT is a language model that has learned to understand the structure and meaning of language and to form new sentences.

Generative AI

When we talk about generative AI, we mean systems that are capable of producing new content. This can include text, images, audio, and even music. Generative AI models create creative outputs based on prompts. Unlike "recognition AI", which only analyzes, generative AI produces new data and text that did not previously exist.

For example, ChatGPT can write texts while Midjourney can generate images from descriptions. These technologies vary greatly in their capabilities and application areas, which is why understanding the terms is crucial for selecting the appropriate technology for your project.

Other Important Terms

Another important aspect is terms like training and inference.

• Training is the intensive process in which a model learns.
• Inference refers to the process in which the trained model provides answers to your prompts.

Additionally, there are parameters and hyperparameters: Parameters are the values that a model learns during training, while hyperparameters are the settings defined before training, such as the learning rate and the amount of data.

The concept of finetuning is also essential. It refers to retraining an existing model on specific data to optimize its performance for particular applications — for example, in the detection of medical images or legal texts.

Application of AI in Practice

Now let's move on to the practical application of all these theoretical concepts. First, we will generate a simple prompt. Let's write an email to a client presenting an offer for social media management.

A simple prompt could look like this: "Write an email to a client with an offer for social media management." However, this is not optimal yet. Now we will optimize this prompt. We will formulate it more precisely and add specific information — such as the client's name and details about the offer.

An optimized prompt could look like: "Compose a friendly and professional email to [Client's Name], presenting an offer for monthly social media management, including three posts per week. Emphasize the added value and ask for feedback."

With this specific prompt, we receive a much better result tailored to the client's needs. The texts for the email to the client are now created.

Now we need a suitable header image for our email. Here, we use Midjourney and also enter a precise prompt to generate the image.

Previously, using Midjourney was limited to Discord servers, but now there are user-friendly interfaces that simplify the process. You can have your image generated simply by describing the desired content, and the result will be created.

Summary – Simple Explanation of AI Concepts: Prompt, Model, and Generative AI

A prompt is the input, and the more precise it is, the better the result will be. The model acts as a learning engine with billions of parameters, while generative AI creates content in various formats. A better understanding of terms like "training," "inference," and "finetuning" helps you optimize the systems and use them effectively. In the live demo, you saw how to further refine a simple example to achieve precise and effective results.

If you have any questions, I am happy to help. I assume that you now have a clear understanding of the central terminology of AI. In the next chapter, we will focus on prompt engineering, where you will learn how to design prompts so that your AI outputs fit from the start.