AI terms explained simply: From Prompt to Generative AI

Dive into the exciting world of Artificial Intelligence! In this guide, you will learn about the key terms you should know to fully leverage the potential of AI tools. Whether you are a beginner or already somewhat familiar with the topic, the clear explanations of terms like “Prompt,” “Model,” and “Generative AI” will help deepen your knowledge. As if that weren't enough, you'll also get a practical insight into the application of these terms with a live demo at the end.

Main Insights

• A prompt is the request you send to the AI. The more precise it is, the better the response.
• A model is the trained AI system that processes these requests.
• Generative AI is the category of AI that is capable of creating new content.

Step-by-Step Guide

What is a Prompt?

A prompt is your own order to the AI. Imagine you are in a restaurant and placing your order. In the world of AI, a prompt is the text you enter into an AI tool to receive an appropriate response or desired outcome. For example: "Write an Instagram post about home office tips."

To optimize the quality of your responses, you should try to formulate your prompt as clearly and precisely as possible. Instead of just entering "home office," you could formulate a specific request, such as: "Write a casual Instagram post with five tips for ergonomic work in the home office."

What is a Model?

The model is the core of the AI application. It is the engine that processes the inputs. A model is a trained AI system that has been fed vast amounts of data, including books and websites. Essentially, the model learns to recognize patterns and perform tasks like text generation or image recognition.

Each model is trained with billions of parameters that help to represent the relationships within the text. When you enter your prompt, these parameters use the trained information to generate an appropriate output. An example of this is ChatGPT-4, a language model that has learned to understand human language and form new sentences.

Generative AI

Generative AI refers to AI systems that are capable of creating new content, whether in the form of text, images, music, or audio. In contrast to so-called recognition AIs, which analyze existing data, a generative AI creates new data that did not previously exist. It is based on previously learned information and provides you with the opportunity to work creatively.

Some well-known applications include ChatGPT, which generates text, DALL-E and Midjourney, which create images from descriptions, and OpenAI's Jukebox, which can compose complete music pieces. This opens up exciting possibilities in creative design and content creation.

Training and Inference

What is the difference between training and inference? They are two central processes in AI development. Training is the labor-intensive process during which the model learns. During this time, the model is supplied with massive datasets and optimized. Inference, on the other hand, refers to the moment when the trained model responds to your prompts.

Parameters vs. Hyperparameters

In AI, a distinction is made between parameters and hyperparameters. Parameters are the values that the model learns during training, whereas hyperparameters are predetermined settings that control the learning process. They determine, for example, how quickly a model is trained on a dataset.

Dataset and Fine-Tuning

A dataset is the complete set of data with which a model is trained. The contents may consist of texts, images, audio, and many other sources. It is also important to mention fine-tuning, which is the targeted retraining of a model with specific data for particular use cases.

An example of fine-tuning would be a model specifically trained on legal or medical texts, such that it performs exceptionally well in this specific area.

Practical Application

With the knowledge gained about prompts, models, and generative AI, you can now formulate targeted, high-quality requests to AI applications. The following example shows how to turn a simple prompt into an effective prompt to achieve better results.

First, use a simple prompt: "Write me an email to a customer with a social media offer." This is very general and is likely to yield insufficient results.

Now let's optimize the prompt: "Draft a friendly, professional email to [Customer Name] offering a monthly social media management package with three postings per week. Emphasize the value added and ask for feedback." By making this specification, you will receive a significantly improved result.

If you want to visualize the offer, you can use Midjourney to create a suitable header image. This can help make the email more engaging and present the content appealingly to the recipient.

Summary – ChatGPT Assistants: Definition of AI Terms Explained Simply

In this guide, you have learned what a prompt is, how models work, and what generative AI means. You now know the differences between training and inference, as well as between parameters and hyperparameters. You have also understood the significance of datasets and fine-tuning. The practical application of these terms shows you how to make your requests to AI more efficient and achieve high-quality results.