Artificial intelligence is more than a hype — it is a new way of working, an amplifier and a mirror of human intelligence. However, in order for humans and machines to truly cooperate in a productive way, clarity about roles, methods, and responsibilities is needed.
Technically speaking, AI consists of software and hardware—that is, the "artificial" components. However, "intelligence" only emerges when humans use it meaningfully, question it, and place it in the right context. Machines imitate human behavior, analyze patterns, and generate texts, images, or decisions based on statistical probabilities. But they understand Neither content nor consequences in a human sense.
Especially in business practice, it is essential not to view AI as an autonomous entity, but as a powerful tool—comparable to a calculator, but significantly more complex. As with any tool, the quality of the results depends on the skill, precision, and intention of the person using it.
Artificial intelligence is not a new phenomenon — but today it can be used in a scalable way.
Even though ChatGPT, Claude, and Gemini are currently receiving enormous attention, the idea of artificial intelligence has been with us for decades. As early as 1997, the chess AI Deep Blue defeated the then world champion Garry Kasparov—a historic moment that demonstrated that machines can simulate cognitive abilities.
Early software solutions like Lotus IBM SameTime or Microsoft's Clippy were also approaches to integrating intelligence into systems to support human work. However, the crucial difference to today's situation is... Computing power, data availability, and model size, which for the first time make modern AI systems suitable for mass use and versatile in their application.
Current examples from practice illustrate how AI already supports administrative and creative processes today:
- Microsoft 365 Copilot It summarizes emails, creates suggested replies, and generates content directly in Word, Excel, or Outlook.
- gamma It develops presentations in seconds that already provide convincing basic structures in terms of style, organization, and narrative.
- Adobe AI Chat It interacts with PDFs, analyzes content, answers questions about documents, or highlights relevant sections.
All these tools show that AI can save time, provide cognitive relief and open up space for strategic tasks — provided it is used correctly.
Why AI doesn't really think yet — and how we can still use it intelligently
A common misconception is that AI "knows" what it's doing. In reality, response generation is based on probabilities. Language models like ChatGPT, Claude, or Gemini use massive datasets to calculate the probability of which word follows another. They can imitate human language—but not its meaning. understand.
This results in two key challenges:
- HallucinationsAI systems “invent” content if they cannot find a suitable data set or if the request is unclear.
- Context blindnessWithout precise information, the system cannot understand what a question refers to — neither professional, cultural, nor technical.
- Our responsibility remains: This makes it all the more important for people to take responsibility — especially in the way they communicate with AI systems.
Methods for improving AI results
A key goal for the productive use of AI is to optimize the quality of the generated content. This can be achieved with the following proven methods:
Individualization of the writing style
Language models can be trained to mimic a user's individual tone or preferred style. To do this, a self-written text is entered into the system, analyzed, and then processed as a so-called... Text briefing saved. In the personalization settings of ChatGPT, for example, this spelling can be permanently saved.
The effect: The AI responds in a more stylistically consistent manner, closer to the user's personality, and with greater relevance. At the same time, the likelihood of hallucinations decreases because the instructions can be interpreted more clearly and coherently.
The four-pillar structure for effective prompting
A Prompt— that is, the input prompt for an AI system — is the core of every interaction. A well-thought-out structure not only increases relevance but also improves the reproducibility of the results. The so-called Four-pillar method is based on the following elements:
- GoalWhat specific output should the AI deliver? (e.g., summary, recommendation, outline)
- contextWhat information is relevant to the task? (e.g., industry, region, target group, purpose)
- ExpectationWhat style, format, or medium is the answer required for? (e.g., factual, inspiring, persuasive.)
- SourcesWhat materials or links should be included?
The last point in particular is often neglected, yet external sources are a crucial lever for in-depth content — especially since many AI models are not connected to the internet by default.
Prompt engineering as a field of competence
For users who want to work systematically with AI, it is worthwhile to use a specialized tool such as the free one. Custom GPT “Your Prompt Engineer”This tool helps formulate and adapt prompts to optimally control the models. The goal is to eliminate uncertainties, create clarity, and strategically manage output—a key success factor in any business context. That's why the term CONTEXT engineering is increasingly used instead of pure prompt engineering.
"Your Prompt Engineer": My free custom GPT helps you formulate and optimize your prompts — valuable support for more precise and efficient input: https://chatgpt.com/g/g-681dbf2c04148191aa86c2457057352e-dein-prompt-engineer
Checking information quality using the CRAP method
AI does not automatically provide valid or verified information. Especially during research, there is a risk of accessing outdated or unreliable content. CRAP method, developed by Sarah Blakesley and Molly Bistrom, offers a pragmatic system for evaluating information sources:
- Current (up-to-dateness)When was the content created or last updated?
- ReliableIs the source primary or secondary? Does it meet scientific or journalistic standards?
- AuthorityWho is behind the information? What expertise is available?
- Purpose (Intention)What was the purpose of formulating the content — informative, sales-oriented, ideological?
This method is particularly important when integrating AI into processes where factual accuracy is crucial — for example, in the areas of communication, education, or strategy development.
New developments: Research with Gemini and Retrieval Augmented Generation
Modern AI systems are developing rapidly. Two particularly relevant developments are currently:
Deep Research with Google Gemini
Google offers a search function called "Gemini", which is known as Deep Research Structured work with AI is made possible. This allows for:
- Define research strategies,
- Verify sources
- Export content directly to Google Docs,
- and even generate infographics, quizzes, or websites from the results.
This is particularly suitable for complex topics that require multi-stage analyses or contextual links — for example, in educational work, strategy development, or content design.
Retrieval Augmented Generation (RAG)
RAG is a new architecture that extends classic language models by adding the ability to actively access external data sources. Instead of relying solely on trained data, a RAG model incorporates current content from databases, websites, or documents. She analyzes it and generates answers based on it, including source citations..
An example of this is NotebookLM, a free tool from Google that:
- Notebooks created with PDF, text, image or audio sources,
- interacts with these sources (via chat or analysis),
- automatically generates structured content such as FAQs, timelines, or mind maps,
- and always refers back to the original source.
A key feature is that content can be shared but not edited—ensuring collaborative security. However, sensitive data should not be uploaded, as this is a cloud service.
An underestimated success factor: friendliness
Finally, a seemingly trivial but scientifically proven point: The clay Communication with AI measurably influences the quality of responses. Studies show that politely worded prompts— including phrases like "please" or "thank you" — not only lead to better results but also increase user concentration and clarity. Friendliness acts as a psychological amplifier here.
Although these formulations have no influence on the calculation from a system perspective, they improve one's own clarity, focus and satisfaction — and thus indirectly lead to better results.
Conclusion and next steps
Artificial intelligence is changing the way we work—not sometime in the future, but right now. Those who not only consume this technology, but consciously engage with it, will benefit from this change. applies, designs and questions, creates a real strategic advantage.
The most important finding: AI is not the goal — it is a tool. And like any tool, it only unfolds its full potential when the person knows what they are doing.
For confident use in practice, the following is recommended:
- Create a personalized text briefing for your AI environment
- Use structured prompts based on the four-pillar method.
- Integrate your own sources to increase relevance
- Verify research results using the CRAP method.
- Experiment with Gemini and NotebookLM for complex tasks
- Observe how kindness affects the quality of your results.
In this video, I'll show you how to better understand AI systems, use them more effectively, and optimize them with simple methods. The goal: Save time, increase quality, avoid hallucinations — and strategically leverage human strengths.
