Introduction
Imagine you're having dinner with friends. The conversation turns to work, and suddenly someone says, "Everyone at our place is using ChatGPT now. That's this artificial intelligence, you know?" You nod, even though you're not quite sure what exactly that means. Artificial intelligence, machine learning, neural networks—these terms are buzzing around in the news, podcasts, and conversations. But what do they actually mean? And most importantly: Do I need to understand all of this to use AI?
The good news upfront: NO, you don’t need to go back to school for computer science. Just as you don’t need to know how an internal combustion engine works to drive a car. However, a basic understanding helps you use AI tools more confidently and better assess what you can expect from them. That’s exactly what this article is about: We’ll sort out the most important terms, look at how AI actually "thinks," and clear up some misunderstandings.
AI, Machine Learning, ChatGPT – Sorting Terms
When people talk about artificial intelligence, they often mix up different terms. This is understandable, as the boundaries sometimes blur. So let’s create some order without jargon.
Artificial Intelligence (AI) is the umbrella term. It describes computers or programs that perform tasks that would normally require human intelligence. This can be many things: understanding text, recognizing images, making decisions, or translating language. AI is like a big umbrella under which many different technologies find their place.
Imagine you go to a supermarket. “Groceries” would be the umbrella term, just like AI. Under it, you find fruits, vegetables, dairy products, and much more. Similarly, there are various approaches and methods under the umbrella of AI.
Machine Learning is one of these methods and currently the most important one. The basic idea: Instead of prescribing exact rules to a computer, you show it many examples. The computer then finds patterns and relationships on its own. It’s a bit like how children learn. No one explains to a toddler exactly what features define a dog. Instead, it sees many different dogs, and eventually recognizes: This is a dog, this is a cat.
Deep Learning takes it a step further. Here, we use so-called neural networks, structures that are roughly inspired by the human brain. These networks have many layers (hence “deep”) and can recognize particularly complex patterns. Most modern AI systems you use today are based on deep learning.
And then there are ChatGPT, Claude, Gemini, and other names you’ve probably heard. These are concrete products, AI systems that you can use directly. They belong to the category of so-called Large Language Models (LLMs). These models have been trained on gigantic amounts of text and can therefore understand and generate language.
The connection is as follows: ChatGPT is a product based on a Large Language Model that has been trained with deep learning, a method of machine learning, which in turn is a subfield of artificial intelligence.
Sounds complicated? Here’s the simplified version:
AI = the big framework, the overall concept
Machine Learning = an important method of how AI learns
Deep Learning = a particularly powerful form of machine learning
ChatGPT, Claude & Co. = concrete tools you can use
This basic understanding is completely sufficient for practical everyday use. You don’t need to know every technical detail to use AI effectively, just as you don’t need to have studied electrical engineering to operate your smartphone.
How Does AI "Think"? (Without Technical Jargon)
This is a question I hear very often, and it’s a valid one. The answer surprises many: AI doesn’t actually think, at least not in the way we humans do.
Let me explain this with an image. Imagine an incredibly well-read person who has read every book in the world, every article, every letter, every email. This person has processed billions of texts and learned which words typically follow each other, which phrases are used in which context, and how sentences are usually structured.
If you ask this person a question, they don’t search an archive for the right answer. Instead, they generate a new text, word by word, based on everything they have read. Statistically, they know which word is likely to come next and choose it.
This is similar to how a language model like ChatGPT or Claude works. They have been trained on unimaginably many texts. In doing so, they have learned patterns: Which words often go together? How are questions typically answered? What information belongs to which topic?
When you ask the models a question, they generate an answer word by word, from left to right. Each word is chosen based on probabilities: “Typically, this word follows those words.”
This also explains some peculiarities of AI systems:
Why do the answers often sound so fluent? Because the model has learned how natural language sounds. It has seen millions of well-written texts and mimics those patterns.
Why does AI sometimes make mistakes? Because it doesn’t “know” facts in the actual sense. It has learned patterns and reproduces them. If something was rare in the training data or contradictory, AI can get it wrong. Experts call this “hallucinations,” where AI invents something that sounds plausible but is incorrect.
Why can the same question yield different answers? Because an element of randomness plays a role in word choice. The model doesn’t always choose the most probable word but varies slightly; otherwise, all answers would be identical and very predictable.
Here’s an analogy that I find particularly helpful: Think of a very experienced translator. They know both languages inside and out and have translated countless texts. When they translate a new sentence, they don’t pull every word from a dictionary. Instead, all their experience and linguistic intuition come into play. They “know” intuitively how the sentence should sound.
This is similar to how AI works, only without real understanding, without consciousness, without personal experiences. It simulates something that looks like understanding, but at its core, it’s highly complex pattern recognition and reproduction.
Why is this important for you? Because it helps you realistically assess AI. You’re not communicating with an all-knowing oracle. You’re using a tool that can handle language impressively well, but it also has its limits, and we’ll look at those limits now.
What AI Can and Cannot Do (Realistic Expectations)
In conversations about AI, I often encounter two extremes: Some expect AI to be able to do everything and soon take over the world. Others wave it off and say, “It’s just a hype that will pass.” The truth lies, as so often, in the middle.
What today’s AI systems can do well:
Generate and edit texts – This is the most obvious strength. AI can formulate emails, write summaries, transform texts into a different style, or develop ideas. Whether you need a complaint letter to your landlord or a product description for your online shop: AI systems often do remarkably good work here.
Translate languages – The quality of AI translations has dramatically improved in recent years. For most everyday purposes, they are absolutely usable. There are still limits with specialized texts or literary works, but for a menu on vacation or an email to a foreign business partner, it works excellently.
Explain and prepare information – Explaining complicated topics simply, translating technical terms, presenting relationships: This is one of the strengths of language models. You can ask: “Explain the basics of accounting” or “What do I need to consider in a rental agreement?” and often receive helpful, understandable answers.
Support creativity – Whether brainstorming for a company name, generating ideas for a blog post, or providing wording assistance for a speech, AI can serve as a creative sparring partner. It doesn’t replace your creativity, but it can stimulate and expand it.
Structure and organize – Summarizing long texts, turning bullet points into prose, creating outlines—AI helps bring order to thoughts.
What today’s AI systems cannot do:
Reliably verify facts – This is important to understand. AI does not have a built-in fact database that it queries. It generates answers based on patterns. Sometimes these answers are correct, sometimes not. Especially with specific numbers, dates, quotes, or lesser-known topics, you should remain critical and verify if in doubt.
Predict the future – Even though AI sometimes sounds like it knows everything, it does not know current events (unless it has a web search function) and cannot make predictions that go beyond statistical patterns.
Replace real expertise – AI can help you prepare for a doctor’s visit or understand legal basics. But it does not replace a doctor, a lawyer, or a tax advisor. For important decisions in these areas, human expertise remains indispensable.
Truly understand context – AI processes what you write to it. It does not know your history, your feelings, or the unspoken connections. If you write, “The meeting was difficult,” the AI does not know that you have been having conflicts with a specific colleague for months unless you explain it to it.
Make moral judgments – AI can present different ethical perspectives, but it has no moral awareness of its own. It does not make real decisions and does not take responsibility.
An example from everyday life: You ask an AI to help you with your tax return. It can explain which expenses are generally deductible, help you understand forms, and check your wording. But whether your specific situation allows for a particular tax deduction can only be definitively assessed by a tax advisor, and in the end, you bear the responsibility for your tax return.
The realistic expectation is: AI is a powerful tool that facilitates many tasks. But it is not a substitute for human judgment, expertise, and critical thinking. Use AI as support, not as a replacement.
The Difference Between AI and a Search Engine
“Can’t I just Google it?” I often hear this question when people first learn about AI assistants like ChatGPT or Claude. It’s absolutely valid, and the answer is: Yes, sometimes. But often not. Let me explain why.
A search engine like Google works fundamentally differently than an AI language model. Here are the key differences:
The search engine finds, the AI generates.
When you search for something on Google, the search engine scans the internet for pages that match your query. It then shows you a list of links. You have to find the actual information on those pages yourself.
AI, on the other hand, generates a direct answer. It summarizes, explains, structures, and delivers the result in the form you need.
A concrete example: You want to know how to bake sourdough bread.
On Google, you type in “sourdough bread recipe” and might get 2 million results. You click through various pages, compare recipes, skim blog posts with life stories before finally finding the actual recipe.
With AI, you can ask: “Give me a simple sourdough bread recipe for beginners, explained step by step.” You’ll receive a clear, structured answer tailored to your request.
The search engine shows sources, the AI formulates itself.
On Google, you see where the information comes from. You can evaluate the source, verify it, and compare different perspectives.
With AI, you often don’t see that directly. The answer sounds as if it comes from a single, authoritative source, but in reality, it is composed of many training patterns. This makes fact-checking more important.
Search engines are current, AI models have a training date.
Google scans the internet in real-time. Current news, new developments, price changes—all of this a search engine finds immediately.
AI language models were trained at a specific point in time. Without an additional web search function, they know nothing about events after that date. If you ask, “Who won the football game yesterday?” an AI without internet access will have to pass.
AI understands conversation, search engines understand keywords.
On Google, you have to think in keywords: “Termination employer deadline Germany.” The better your keywords, the better the results.
With AI, you can talk like you would with a person: “My boss wants to fire me. What deadlines does he have to meet, and what are my rights?” The AI understands the context and can respond accordingly.
When to use what?
Use a search engine when you need current information, when you want to see the original source, when you want to compare prices, or when you want to buy something.
Use AI when you need explanations, when you want something formulated, when you want to brainstorm ideas, when you need something summarized, or when you’re looking for a creative sparring partner.
In practice, both complement each other wonderfully. You can use AI to understand a topic and then delve deeper with targeted search engine queries. Or you can first research with Google and then have AI help you summarize your findings.
By the way, many modern AI systems now have a built-in web search function. This allows them to retrieve current information from the internet and combine it with their summarization ability, providing the best of both worlds.
Why AI Does Not Require Programming Skills
Now we come to a point that relieves many people: You do not need programming skills to use AI. None. Zero. Really not.
This wasn’t always the case. Just a few years ago, AI systems were actually only accessible to specialists. You had to write code, prepare data, and train models. This required a computer science degree or at least years of training.
Today’s AI assistants are different. They have an interface that we all master: language. You write what you want, and the AI responds. No more, no less.
Imagine you’re learning to drive a car. You don’t need to know how the engine works. You don’t need to understand what happens in the transmission. You learn: steering wheel, pedals, mirrors, traffic rules. That’s enough to get safely from A to B.
It’s the same with AI. You learn: How do I formulate my requests (prompts) so that I get good results? How do I deal with the answers? When is AI helpful, and when is it not? That’s all.
What you really need:
Basic computer skills – If you can surf the internet, write emails, and operate a browser, you’re ready.
Curiosity and willingness to experiment – AI systems sometimes respond unexpectedly. The more you try, the better you understand how to use them optimally.
Patience with yourself – No one becomes an AI expert overnight. The good news: You can’t break anything. Just try it out.
Critical thinking – As we discussed, AI is not infallible. Question the results, especially on important topics.
What you definitely do not need:
Programming languages – You write normal German (or any other language), not code.
Mathematical knowledge – The complex mathematics behind AI happens in the background. You don’t need to understand any of it.
Technical background knowledge – You don’t need to know what a neural network is to chat with ChatGPT. It helps with understanding (that’s why this article), but it’s not a prerequisite.
Expensive software or hardware – Most AI tools run in a browser on any normal computer or even on a smartphone.
An encouraging example: I know a student who uses AI to get complicated physics concepts explained in simple words and to structure his presentations. A stonemason in his late forties who uses it to formulate cost estimates and professionally respond to customer emails. An accountant who uses AI as a sparring partner to make tricky issues understandable for clients. None of them has programming skills, but all benefit from the technology.
The key lies not in technical know-how but in communication. And this blog series helps you with that. Step by step, you’ll learn how to address AI tools so that they are genuinely useful to you. The prompt generator we use in every exercise supports you by transforming simple inputs into optimized requests that yield better results.
Exercise: The Three Faces of an Explanation
Now it’s time to get practical. In this exercise, you’ll get to know the prompt generator, a tool that helps you make better requests to AI systems. At the same time, you’ll experience how differently the same task can be approached.
Your task:
Open the prompt generator and select the following in the fields:
Application area: Text
LLM Model: Choose a model of your choice (e.g., ChatGPT or Claude)
Description: I want to understand what artificial intelligence is. The explanation should be simple enough for a child to understand. No technical terms, just everyday language and vivid comparisons.
Goal & Output: A comprehensible explanation of AI in about 5-8 sentences
Tone: Friendly, patient, child-friendly
Now the exciting part: The prompt generator will create three different versions of this request for you:
The structured version is clearly organized, with a logical sequence. Perfect if you want a systematic, complete explanation.
The compact version is short and concise. It gets straight to the point and is suitable when you need a quick, clear answer.
The creative version uses images, stories, or unusual comparisons. Ideal if you’re looking for something memorable and vivid.
What you should do:
Have all three versions generated. Then copy each version one by one into the AI system you selected in the LLM Model. Compare the results: How do the answers differ? Which style do you like best? Which version would you actually read to a child?
Reflection:
You’ll see: The way you ask influences the type of answer enormously. This is one of the most important lessons in dealing with AI. The same question can yield completely different results depending on how it’s phrased. The prompt generator helps you discover these different formulations and choose the one that best fits your purpose.
This exercise is also a test: You have just successfully used an AI tool. No programming, no technical knowledge, just your words and a few filled-out fields. It’s that simple.
Conclusion and Outlook
You now know what lies behind the terms AI, Machine Learning, and ChatGPT. You understand that AI doesn’t really “think,” but reproduces patterns, impressively well, but with limits. You can assess what AI is useful for and where you should be cautious. And you have experienced that using AI is not rocket science.
The most important thing: You don’t need technical knowledge. You just need the willingness to try something new, and you’ve already proven that by reading this far.
In the next article, “ChatGPT, Claude, Gemini & Co. Understanding the AI Landscape,” we’ll look at the specific AI systems you can use today. What exists, how do they differ, and which one is suitable for what? With that, you’ll be well-equipped to choose your personal AI tool.
The journey into the world of artificial intelligence has just begun, and you are part of it.