Generative artificial intelligence is easy to use. Just enter a question, wait a moment, and an answer appears. However, it is precisely this simplicity that can obscure just how much the quality of the result depends on how a task is presented to the AI.
An unclear prompt often results in a generic response. If important context is missing, the AI must make assumptions. If the goal, requirements, or desired outcome are unclear, even a powerful AI system can be of only limited help.
This is giving rise to a new skill for everyday work: Prompt Engineering.
But what does "prompt engineering" actually mean? Do employees have to learn complicated commands to use it? And what distinguishes a good prompt from an ordinary question?
The most important takeaway is this: Good prompts are not magic formulas. They stem primarily from clear instructions, relevant context, and the ability to critically evaluate the results afterward.
What is prompt engineering?
A prompt is, first and foremost, any input a person uses to assign a task to a generative AI system. This can be a simple question, a set of instructions, or a detailed description of a task with additional information.
Prompt engineering takes it one step further.
This refers to the ability to consciously structure such inputs in a way that the AI can understand them as well as possible:
- what is to be achieved
- which context is relevant
- What are the requirements?
- what the result should look like
- which limits must be taken into account
This means that prompt engineering is less technical than the term might initially suggest.
In everyday professional life, one skill that companies are already familiar with from other areas often comes into play: clearly defining tasks.
Someone who simply tells a colleague, “Please make something for me,” will likely get a different result than someone who explains the goal, target audience, context, and expectations.
It's similar with generative AI.
Why Good Prompts Are Becoming More Important in the Workplace
The more frequently AI is used in day-to-day work, the greater the impact the quality of the input has on the result.
This goes far beyond just writing texts.
Generative AI can, for example, help with the following:
- Organizing Information
- Summarizing documents
- Developing ideas
- Preparing content
- to present complex issues in a more understandable way
- Formulating questions based on available information
- Organizing Work Tasks
This also changes the role of the user.
It is no longer enough to simply open and use an AI tool. Employees must learn to describe a task in a way that allows the AI to provide meaningful support.
Prompt engineering thus becomes a practical component of AI expertise.
A good prompt starts before you begin typing
People who want to learn prompt engineering often start by looking for formulas, templates, or the “best prompts.”
Templates like these can be helpful. However, they do not solve the actual problem.
After all, before writing a prompt, it must first be clear what the goal is.
Let's take a very simple query:
"Write me an essay on project management."
The AI knows neither the target audience nor the purpose, the desired length, nor the focus. It must fill in this information on its own and will therefore likely produce a general result.
It would be much more helpful to have a task description that explains, for example, that the text is intended for managers, is meant to clearly explain a specific aspect of project management, and is required in a defined format.
The key difference does not lie in any particular AI trick.
It lies in the clarity of the task.
Five elements that help you write good prompts
Not every request requires a long prompt. However, for more complex tasks, a few basic elements can be helpful.
1. The Goal
What is the end result supposed to be?
Should the AI generate ideas, summarize existing content, highlight differences, or create a structure?
The more clearly the goal is defined, the easier it is to assess whether the result is actually helpful.
2. The Context
What information does the AI need to correctly classify the task?
These may include, for example, the target audience, the initial situation, existing content, or specific framework conditions.
It is precisely this context that is missing from many weak prompts.
3. The Requirements
What criteria should the result meet?
These could include, for example, tone, scope, key priorities, or aspects that should not be taken into account.
It is important to include only those requirements that are actually relevant to the specific task.
4. The Desired Result
How should the answer be structured?
A continuous text requires a different structure than a summary, a table, or a decision-making guide.
Describing the desired result makes the task easier for the AI and often reduces the amount of follow-up work required.
5. The Exam
What needs to be checked afterward?
This point often doesn't even appear in the prompt itself, yet it is still part of professional prompt engineering.
This is because a well-formulated prompt increases the likelihood of a useful result. However, it does not guarantee that the result is correct.
Good prompts don't necessarily mean long prompts
A common misconception is that professional prompts need to be as comprehensive as possible.
That's not true.
A short prompt may be perfectly sufficient for a simple task. A longer prompt, on the other hand, may not work as well if it contains contradictory, unnecessary, or unclear requirements.
Therefore, the number of words is not what matters.
The key factor is whether the relevant information is included.
A good prompt is as detailed as necessary and as clear as possible.
Prompt engineering, therefore, also means being able to distinguish between important and unimportant information.
Prompting is often a conversation rather than a single command
The expectation that you have to write the perfect prompt on the very first try also often leads you in the wrong direction.
Working with generative AI is an iterative process in many situations.
For example, an initial result may indicate that a task has not yet been described with sufficient precision. In response, the context can be expanded, a requirement can be clarified, or a specific part of the result can be examined more closely.
In simple terms, the process might look like this:
Formulate the task, check the result, improve the prompt, and re-evaluate the result.
This makes prompt engineering a form of collaboration with the AI system.
While anyone who simply accepts a result is using AI, those who recognize why an answer doesn't yet fit the task and how the prompt can be improved are developing true prompt proficiency.
Expertise Is Not Made Obsolete by Prompt Engineering
The better AI systems become at formulating ideas, the easier it is to get the impression that expert knowledge is becoming less important.
In prompt engineering, the opposite is often the case.
If you know little about a topic yourself, it can be difficult to judge whether important information is missing, whether statements are plausible, or whether an answer merely sounds convincing.
Professional expertise is therefore helpful in several ways:
- formulating the right task
- to provide important context
- identifying inappropriate assumptions
- Evaluating Results
- to ask relevant questions
- To identify errors or gaps
So a good prompt is no substitute for expert knowledge.
It helps to combine domain expertise more effectively with the capabilities of generative AI.
Why Testing Is More Important Than the Perfect Prompt
Prompt engineering is sometimes portrayed as if any problem could be solved with the right phrasing.
That would be a dangerous expectation for use within a company.
Even a very good prompt can result in a response that is incomplete, inappropriate, or factually incorrect.
That is why professional use of AI always consists of at least two parts:
Good input and critical analysis.
This is exactly where Prompt Engineering ties in with the topic of AI expertise.
Employees must not only learn how to get better answers; they must also be able to determine which answers they can trust and which ones need to be verified.
This is especially true when AI output is subsequently used as the basis for decisions, customer communications, or other relevant workflows.
Good prompts require good information
The quality of the context provided also plays a crucial role.
If incorrect, incomplete, or outdated information is included in a task, a well-formulated prompt cannot automatically resolve this issue.
In terms of day-to-day business operations, this means:
Anyone who works with AI must do more than just ask questions. They must also consciously choose which information is appropriate for the task at hand.
At the same time, internal rules regarding data protection, information security, and confidentiality remain in effect.
Just because certain information could improve an AI's response does not automatically mean that it should be entered into every AI system.
Prompt competence, therefore, also includes the ability to handle context responsibly.
Prompt engineering is more than just using ChatGPT
The term "prompt engineering" is often associated directly with ChatGPT.
That's understandable, but it also falls short.
Prompts always play a role when working with generative AI systems. The specific interface or tool used may change, but the underlying skill remains relevant.
That is exactly why it's worthwhile for companies to do more than just explain to employees how to use a specific tool.
Transferable skills are more helpful in the long run:
- Describe tasks precisely
- recognize the necessary context
- Formulating Expectations
- Evaluate Results Critically
- Ask questions
- Iteratively Improve Inputs
This ensures that the skills acquired remain valuable even as the AI tools used evolve.
Prompt libraries can help, but they cannot replace expertise
Many companies start by collecting successful prompts and making them available to their employees as templates.
That might make sense.
After all, recurring tasks don't have to be described from scratch every time. Good examples can also illustrate what a clear task description looks like.
It only becomes a problem when templates are adopted without understanding them.
A prompt that works exceptionally well for a specific use case may be unsuitable in a different context. Target audiences change, information varies, and the desired output may also be different.
A prompt library should therefore not convey:
"Copy this text, and you'll always get the right result."
Rather:
"Here's an example of what a good task description might look like. Adapt it to your context."
Templates support expertise. They do not replace it.
Companies don't need *the* perfect prompt
Prompt engineering should not become too complicated at the organizational level either.
Not every team needs extensive prompt guides or rigid guidelines for every AI query.
Few shared principles could be more meaningful.
For example:
- Clearly state your desired goal.
- Provide only relevant and permissible context.
- Describe key requirements.
- If necessary, specify the desired output format.
- Evaluate the results based on their significance.
- If the result does not match the problem, adjust your input.
Such principles provide guidance without unnecessarily restricting practical use.
The Role of Prompt Engineering for Executives
Executives don't necessarily have to write a company's best prompts.
However, you should understand how the quality of prompts affects the results of generative AI.
This is especially important when AI is integrated into workflows.
When a team works with AI on a regular basis, questions such as the following arise:
- What tasks are suitable for generative AI?
- What information may be used?
- What results are required for an exam?
- When are reusable prompt templates helpful?
- What skills do employees need?
- Who is responsible for the outcome?
This makes prompt engineering a leadership issue as well.
Not because managers should monitor every prompt, but because they need to create an environment in which employees can use AI effectively.
The best way to learn prompt engineering is through hands-on practice
Prompt engineering can only be learned to a limited extent through a list of rules.
What matters most is practical application.
If you try out different phrasings, compare results, and refine your own prompts, you'll gradually develop a better sense of what information is important for a given task.
That is exactly why prompting is also part of the A4Q AI Foundation training at SERVIEW. The course covers the importance of prompts, fundamentals, and best practices, as well as examples of good and less suitable prompts. Participants create, optimize, and reflect on their own prompts based on realistic work scenarios.
Prompt engineering is not considered in isolation. The training course integrates this topic with generative AI, practical use cases, risks, legal frameworks, and the integration of AI into business processes.
This also reflects practical needs: A good prompt is valuable. However, it is crucial to use it responsibly and in the right context.
Questions Companies Should Answer Now
If you want to actively promote prompt engineering within your company, you can start by asking a few simple questions:
- What tasks is generative AI already being used for today?
- Do employees know how to clearly articulate goals and context?
- Are AI results subsequently reviewed thoroughly?
- Are there any recurring tasks for which prompt templates would be useful?
- Do employees know what information they are allowed to use as context?
- Can you tell when a prompt needs to be improved?
- Is prompting taught as part of a broader AI curriculum?
- Are there appropriate learning opportunities for different levels of experience?
Once these questions have been answered, prompt engineering can be developed in a much more targeted way than through a loose collection of supposed “super prompts.”
Conclusion: Good prompts start with clear thinking
Prompt engineering is one of the most visible new skills in working with generative AI. But the real expertise doesn't lie in writing the most complicated prompts possible or knowing secret formulas.
Good prompts result when people understand their task, recognize relevant context, and clearly articulate expectations.
What happens afterward is just as important.
Results must be evaluated, questioned when necessary, and reused responsibly. That is precisely why prompt engineering is not a substitute for AI expertise, but rather a part of it.
Anyone who wants to learn prompt engineering should therefore do more than just look for better phrasing. What matters most is the ability to formulate tasks more precisely, evaluate results critically, and improve collaboration with generative AI step by step.
This is how simple prompting becomes a skill that can add real value in everyday work.
Most Recent
Would you like to start by understanding what skills employees and managers need to use artificial intelligence responsibly? Then be sure to read the previous post:
“AI Literacy in the Workplace: What Employees and Managers Need to Know Today”
Training Tip: A4Q AI Foundation at SERVIEW
If you want to not only understand prompt engineering in theory but also apply it in everyday business practice, the A4Q AI Foundation training course at SERVIEW is the perfect place to start. In addition to the fundamentals of artificial intelligence and generative AI, you’ll learn how to develop effective prompts, optimize inputs, and evaluate the quality of results. The training combines prompting with concrete business applications and the responsible use of AI.
Learn more:
A4Q AI Foundation Training at SERVIEW
