Artificial Intelligence (AI) is the umbrella term for technologies that enable machines to exhibit intelligent behavior – from simple decision trees to advanced algorithms. A specialized form of this is Generative AI, which is capable of creating entirely new content such as texts, images, or music. Large Language Models (LLMs) like ChatGPT form a special subgroup of Generative AI and are trained to understand and generate language.
These layers build upon each other! Every LLM is GenAI, every GenAI is AI – but not every AI can generate, and not every generative AI is an LLM. In the article, you will find illustrative practical examples, helpful tool tips (including ChatHub*), and comparison tables that will help you use the terms correctly in everyday life. A future outlook also shows why the importance of these technologies will rapidly increase by 2030 – you should know the differences both privately and professionally. AI, Generative AI & LLMs: The 3 levels of modern AI.
AI, Generative AI & LLMs: The 3 Levels of Modern AI
Artificial Intelligence (AI)
Umbrella term for machine intelligence. Encompasses all systems that simulate human intelligence – from simple rules to complex machine learning.
Generative AI (GenAI)
Generates new content such as texts, images, code, or music. Uses generative models to create creative outputs based on learned patterns.
Large Language Models (LLMs)
Language models such as ChatGPT or Claude. Prediction machines that generate the most probable word sequence based on billions of text data.
You constantly hear terms like AI,Generative AI, and LLMs, and they are often thrown around interchangeably. ChatGPT,Gemini,Copilot – these names are popping up everywhere. But what’s really behind them? It’s easy to lose track. This article sheds light on the matter. You will learn what these terms mean, how they are related, and what the exact differences are. In the end, you will understand why ChatGPT is not just an AI, but something much more specific. As an AI agency with a focus on generative AI since 2018, we know very well what is possible.
AI simply explained: From Artificial Intelligence to ChatGPT & Co
Artificial Intelligence (AI) is a huge field. Think of it as the umbrella term for all technologies that enable machines to exhibit human-like intelligence to demonstrate. The goal is to teach computers to perform tasks that normally require human thought. This includes learning,problem-solving,pattern recognition, and language understanding.
Think of AI like the concept of „vehicle.“ A vehicle can be a bicycle, a car, a bus, or an airplane. All serve the purpose of transportation, but they function very differently and are built for different purposes. It’s the same with AI. An AI that plays chess is different from one that filters spam emails or recognizes faces in photos.
Modern tools like ChatGPT,Gemini and Copilot (AI Companion) are concrete applications based on specific AI technologies. They are, so to speak, the „sports cars“ among vehicles – highly specialized and powerful. They impressively demonstrate what is possible today, but are only a small part of the entire world of Artificial Intelligence.
AI → GenAI → LLMs: The 3 Levels of Modern AI
To understand current developments, it helps to divide modern AI into three levels. Imagine them like Russian Matryoshka dolls: Each level is part of the next larger one.
Level 1: Artificial Intelligence (AI)
This is the outermost, largest doll. As already mentioned, AI is the broad field that encompasses all forms of machine intelligence. This also includes „classic“ AI systems that do not necessarily create new content. A navigation system that calculates the fastest route, or an algorithm that predicts stock prices, are examples of AI. These systems often operate according to fixed rules or recognize patterns in existing data.
How do AI systems work?
Classic AI is often based on algorithms, fixed rules, and predefined decision trees. It usually uses structured data, analyzes it for specific patterns, and arrives at a result – for example, a recommendation or a prediction. A spam filter, for instance, recognizes whether an email is unwanted based on keywords and patterns.
Application examples for AI:
- Navigation apps like Google Maps, which suggest the best route for you
- Spam filters in your email inbox
- Creditworthiness checks at banks
- Facial Recognition to Unlock Your Smartphone
2nd Level: Generative AI (GenAI)
Inside the large AI doll is Generative AI. This is a subfield of AI that specializes in creating new, original content. Instead of just analyzing data or recognizing patterns, GenAI creates something that did not exist before. This can be texts, images, music, code, or even videos. It learns from vast amounts of data and uses this knowledge to generate new things creatively. Tools that paint a picture from a text description are a perfect example of Generative AI.
How do Generative AI models work?
Generative AI mostly uses neural networks, especially so-called Generative Adversarial Networks (GANs) or Transformer architectures. They are trained on large datasets and thereby learn regularities, structures, and features of existing content. Afterwards, they are able to produce completely new results that resemble the training material – for example, a new image, a piece of music, or a text.
Application examples for Generative AI:
- Image generators such as DALL·E or Midjourney, which create images from text descriptions
- Music generators, which compose melodies or entire songs
- Code generators, which write program code based on a task description
- AI that synthetically generates videos
Level 3: Large Language Models (LLMs)
The smallest doll in our set is the Large Language Model (LLM). LLMs are a special type of Generative AI. Their specialty is language. They are trained with gigantic amounts of text from the internet, books, and other sources. This allows them to learn the nuances,grammar,context, and even the style of human language. Their main task is to understand and generate language. ChatGPT, Gemini and similar models are LLMs. They are the technological basis for the chatbots with which you can converse.
How do Large Language Models work?
LLMs are based on so-called transformer networks. When generating text, they analyze vast amounts of words and their relationships. During training, they learn which words and phrases typically follow each other. When you ask a question or enter text, the model calculates which is the most likely sensible next word – and thus, a coherent, understandable text is gradually created. Thanks to their size (sometimes billions of parameters), LLMs are astonishingly good at recognizing context and linguistically implementing complex tasks.
How do LLMs work?
The Librarian
Attention mechanism: Searches all shelves simultaneously, recognizes connections.
Generate
Output
An LLM is not a database. It is more like a librarian who has learned from billions of pages read how language is structured. When needed, she looks up information in the archive using RAG/tools to remain factually accurate.
Application examples for LLMs:
- Chatbots like ChatGPT, Gemini, or Copilot, to answer questions or write texts
- Automated email responses and text summaries
- Translation services by LLMs that transfer understandable texts into different languages
- Text Generation for Social Media Posts, Blog Articles, or Creative Stories
In summary: Every LLM is a form of Generative AI, and every Generative AI is a form of Artificial Intelligence. But not every AI is generative, and not every Generative AI is a language model.
Now it gets interesting. If someone says: „ChatGPT is an AI„, that’s technically not wrong, but extremely imprecise. It’s like pointing at a Porsche 911 and saying: „That’s a vehicle.“ Yes, that’s true, but it leaves out all the important details that make it special.
ChatGPT is much more than just a general AI. It is a concrete application based on a Large Language Model (LLM). This LLM, in turn, is a form of Generative AI (GenAI).
The statement is therefore incomplete because:
- It ignores the ability to generate:ChatGPT does not just analyze data. Its core competence is to generate new texts. It writes emails, composes poetry, summarizes articles, and answers your questions in fluent prose. This is a capability that many other AI systems do not have.
- You omit the specialization in language:ChatGPT is an expert in language. It has been specifically trained to understand and imitate human communication. An AI for facial recognition or robot control has completely different capabilities.
The more accurate description would therefore be: ChatGPT is an application that uses a Large Language Model (a specialized form of Generative AI) to conduct human-like conversations and create texts. While this is a long sentence, it precisely describes what the technology is all about. So, the next time you hear someone talking about „the AI“ when they mean ChatGPT, you’ll know better.
Comparison Matrix: AI vs. Generative AI vs. LLM
This table summarizes the key differences for you:
| Criterion | Artificial Intelligence (AI) | Generative AI (GenAI) | Large Language Model (LLM) |
|---|---|---|---|
| Definition | An umbrella term for all technologies that enable machines to exhibit intelligent behavior. | A subfield of AI that creates new, original content. | Specialized GenAI trained to understand and generate human language. |
| Main Task | Solve problems, recognize patterns, make decisions, automate processes. | Generate content (text, image, audio, code, etc.). | Understand, summarize, translate text, and create new text. |
| Output | A prediction, a classification, an action (e.g., calculating a route). | New content that did not exist before (e.g., an image, a poem). | Natural-sounding text (e.g., an answer, an email). |
| Example | Spam filter, navigation system, chess computer, recommendation algorithm. | Image generators (e.g., Midjourney), music composition tools. | ChatGPT, Google Gemini, Claude. |
| Analogy | The concept of „vehicle“ | The vehicle type „car“ | The specific model „electric sedan“ |
Top 10 Tools for AI, Generative AI & LLMs
Here you will find an overview of the currently hottest tools in the field of Artificial Intelligence,Generative AI and Large Language Models – including a brief description:
- ChatGPT
The most well-known LLM from OpenAI, with which you can generate texts, answer questions, or automate tasks. - Google Gemini
Google’s multimodal LLM that understands and processes text, images, and code – ideal for creative and analytical applications. - Microsoft Copilot
AI assistant integrated into Microsoft products like Office for creating texts, presentations, and more. - Tip: ChatHub*
An AI tool for chatting with multiple LLMs simultaneously – perfect if you want to compare or combine different models. - Claude
Anthropic’s advanced language model, known for high-level dialogues and strong content generation. - Midjourney
A generative AI tool, particularly used for impressive image generation from text descriptions. - DALL·E
OpenAI’s AI model for the creative generation of new images through text input. - Jasper
A powerful AI assistant for marketing, blog articles, and content creation at the touch of a button. - Synthesia*
Generates realistic-looking AI videos from written text – useful for tutorials and explainer videos.
Alternative: Colossyan* - Perplexity AI
A search and answer tool based on LLMs that answers complex questions at lightning speed and directly cites sources.
Why everyone should use ChatHub:
ChatHub unites all 3 levels of AI with all major LLMs and offers Generative AI directly based on many models
Test ChatHub now and discover the possibilities of modern AI!
The terms AI, Generative AI and LLM describe different levels of a fascinating technology. Artificial Intelligence is the broad framework, Generative AI is the creative creator within it, and Large Language Models are the language artists.
If you understand this difference, you can better classify current developments. You now know that tools like ChatGPT are not just „some AI,“ but the result of highly specialized developments in the field of language processing. With this knowledge, you are well equipped to understand and help shape the exciting future of Artificial Intelligence.
ChatHub* offers a cost-effective and efficient way to gain a global overview of the world of Artificial Intelligence. The tool combines all three levels – from classic AI to Generative AI and advanced Large Language Models (LLMs) – in a single, easy-to-use platform. This allows ChatHub not only for versatile applications, but also for a better understanding and direct comparison of different AI technologies at an affordable price.
Blog article on ChatHub:
One tool, up to six AIs simultaneously: ChatHub changes the way you work with LLMs & AI chatbots
Practical Examples: AI in Various Industries
| Industry | Practical Examples | Tools/Technologies |
|---|---|---|
| Marketing | Automated ad copy, personalized email campaigns, target group segmentation, customer behavior prediction, market trend analysis | Jasper, ChatHub, Copilot |
| Industry | Quality control with image recognition, predictive maintenance, production optimization, robotics in manufacturing | IoT sensors, AI analytics |
| E-commerce | Individual product recommendations, dynamic pricing,AI-powered chatbots, automatic image recognition for product categorization | ChatHub, LLMs, Recommendation Engines |
| Medicine | AI-assisted diagnostics (e.g., radiology), intelligent health chatbots, therapy recommendations, data analysis for research | MedGPT, image recognition tools, ChatHub |
| Education | Automated grading, AI learning platforms, individualized learning paths, interactive teaching materials, translation & accessibility | ChatGPT, Gemini, Synthesia |
The development of Artificial Intelligence will advance rapidly in the coming years. According to a PwC study,AI could generate a global economic output of up to 15.7 trillion US dollars by 2030. The share of Generative AI is continuously growing, especially through new applications in the creative sector and in software development and in customer service. Gartner predicts that by 2026, more than 80% of enterprises will have deployed generative AI in some form.
LLMs will also continue to gain importance: By 2030, experts expect them to be able to generate not only texts but also multimodal content such as images, videos, and audio data at the highest level. The continuous improvement of the models ensures greater precision,contextual understanding and adaptability to specific industry needs. Sources such as McKinsey and IDC assume that AI-powered productivity will lead to cost reductions of up to 20% and significant efficiency gains in many areas.
Overall, we are therefore facing a future in which AI, Generative AI and LLMs are an integral part of our daily lives and the economy, opening up ever new, innovative application possibilities.
Florian Ibe
CEO & Marketing Consultant
Your contact person: Florian Ibe
Test ChatHub now and discover the possibilities of modern AI!
The terms AI, Generative AI and LLM describe different levels of a fascinating technology. Artificial Intelligence is the broad framework, Generative AI is the creative creator within it, and Large Language Models are the language artists.
Understanding this difference will help you better classify current developments.
You now know that tools like ChatGPT are not just „some AI,“ but the result of highly specialized developments in the field of language processing. With this knowledge, you are well-equipped to understand and help shape the exciting future of Artificial Intelligence.
FAQ on AI, Generative AI & LLMs
1. What is the difference between AI and Generative AI?
AI is the umbrella term for all technologies that enable machines to exhibit intelligent behavior. Generative AI is a subfield of AI that can create new, original content.
2. What makes Large Language Models (LLMs) special?
LLMs are specialized generative AIs that focus on language. They understand and generate human-like text.
3. What are Generative AIs used for in everyday life?
Generative AI is used in text, image, music, and code generation, as well as for creative and supportive tasks, such as writing emails or designing images.
4. Is ChatGPT a typical AI application?
ChatGPT is more than a classic AI. It is based on an LLM and can not only analyze but also generate new texts – which makes it particularly versatile.
5. Do I need programming skills to work with Generative AI?
No, many AI tools are now designed to be user-friendly and do not require programming skills. You can usually use chatbots or generators directly via websites or apps.
Important Term Definitions
- Artificial Intelligence (AI): Umbrella term for systems that exhibit human-like intelligence.
- Generative AI (GenAI): A subfield of AI that generates new content such as text, images, or music.
- Large Language Model (LLM): Specialized GenAI trained to understand and generate human language.
- Chatbot: Software that communicates automatically with people via text or voice, often based on LLMs.
- Training: The process by which an AI „learns“ with large amounts of data, recognizes patterns, and subsequently performs tasks based on them.
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