What makes a good prompt?

A good prompt clearly describes the desired outcome, provides the AI with useful context, and specifies relevant constraints. The four most important elements are: the task, the context, the desired output format, and the tone or perspective from which the response should be given.

Will better tools make prompt engineering obsolete?

Better tools lower the barrier to entry for simple tasks. For specific, challenging requirements, the ability to formulate clear instructions remains essential. The difference between an average prompt and a good one doesn't get any smaller just because the tools are getting better.

Who is responsible for AI-generated text?

The same person who would be responsible for it even without AI. AI provides a suggestion, not a decision. Whoever approves a text is responsible for it—regardless of how it was created.

Prompt vs. Briefing: What's the Difference?

A briefing is intended for a person who thinks for themselves, fills in the gaps, and asks questions. A prompt must be more complete because, while an AI fills in those gaps, it doesn’t always do so correctly. A good prompt is a complete briefing for a system that doesn’t ask for clarification.

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Prompt Engineering

Better results from AI tools don’t come from better tools, but from better instructions. This insight is simple yet far-reaching: The decisive factor in working with artificial intelligence is not the technology, but the person operating it. For companies integrating AI into their processes, this is not just a technical footnote, but a matter of responsibility. Whoever decides how a prompt is formulated also determines the quality of the final output.

This article explains how prompts work, what skills are required for effective prompting in everyday work, and how companies can build prompting expertise.

Written by
Published on
Last modified on

Katrin Kratz
September 23, 2026
September 23, 2026

In this article, you will learn:

Prompt Engineering as a Work Technique

AI generates it, but the final decision rests with humans.

Building Prompt Competence in the Workplace

Why Better AI Tools Don't Solve the Problem

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Prompt Engineering as a Work Technique

A prompt is the input that a person provides to an AI tool such as ChatGPT, Claude, or Gemini. The precision of this input directly determines the quality of the output. A prompt is thus a delegation of work to a system that does not ask for clarification and does not independently fill in gaps in a meaningful way. What may allow for some room for interpretation in a briefing to an employee becomes a source of error in prompting.

A good prompt usually consists of four elements:

The task: What exactly should the AI do?
The context: To which situation, target audience, or channel does this apply?
The Format: What should the final product look like in terms of length, style, and structure?
The tone or the perspective: From what standpoint should one write?

The answers to these four questions form the basis for prompts that actually yield useful results. Prompt engineering is therefore not a niche skill, but a working technique that becomes relevant wherever AI tools are integrated into processes.

AI generates it, but the final decision rests with humans.

AI language models process language and generate high-quality content. However, they do not understand the intent behind a query, do not set goals, and cannot assess the quality of their own output. This is the fundamental logic behind these systems, and it explains why the responsibility for producing good results cannot simply be outsourced.

Clarity Before the Prompt

If you don’t know what you want, you’ll end up with a result that sounds coherent but misses the actual goal. AI tools significantly speed up content production. But if you don’t know what you want to produce, these tools will only give you poor results faster—not better ones. Speed, therefore, is not a way out of unclear thinking; rather, it merely amplifies the scale at which that lack of clarity takes effect.

From Creation to Evaluation

When AI is integrated into the process, the focus shifts: away from creation and toward review, classification, and adaptation. This is the real change that many companies underestimate. A text that is linguistically polished but off the mark in terms of content is harder to spot than one that is poorly written. Anyone who cannot independently assess the quality of an output is handing over control of the result to a system that cannot bear that responsibility.

This is precisely where the advantage of experienced marketing and communications professionals lies. In the past, the ability to express ideas clearly and purposefully was the foundation of good writing. Today, it is the foundation for effectively delegating tasks to AI. Those who know their target audience can explain to the AI who it should address. Those who can assess quality can recognize when a result is appropriate and when it is not. The goal has shifted. The expertise remains.

Building Prompt Competence in the Workplace

Proficiency with prompts isn’t gained through a one-time training session, but through structured discussion and ongoing practice. Anyone who works regularly with AI tools learns which phrasing works and which doesn’t. This practical knowledge shouldn’t be confined to individual notebooks—it’s part of quality assurance, just like a style guide or brand guidelines.

Prompt Collections as Shared Team Knowledge

A pragmatic first step is to build an internal collection of prompts: documented instructions for recurring tasks in day-to-day marketing and communications. These include, for example:

Suggested text for social media channels with a defined tone
Target Audience Descriptions for Campaign Briefings
Content Ideas Based on Existing Topics or Keywords
Revision Instructions for Existing Texts
Templates for editorial plans or content briefings

Collections like these make experiential knowledge shareable, lower the barrier to entry for new employees, and prevent tried-and-true phrasing from being lost with the next staff change. More important than the collection itself, however, is the decision behind it: whoever maintains a prompt collection sets the standard for quality.

Why Better AI Tools Don't Solve the Problem

Many AI providers integrate pre-written prompts in the background that enhance simple inputs. This has its value: Not every task requires in-depth knowledge of prompting. However, as soon as requirements become specific (where tone, context, or target audience play a role), the difference between a general prompt and a customized one remains clear. Background automation doesn’t solve any content-related problems. It merely postpones the point at which one’s own judgment is once again required.

Do you have any questions?

Together, we’ll identify where prompt expertise has the greatest impact in your company: We’ll examine which tasks are suitable for documented prompts and how to set up an internal prompt library that will remain intact even when staff changes occur. In a no-obligation consultation, we’ll show you where it’s worth investing in this system and where you’re better off without AI. Just get in touch with us.

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Frequently Asked Questions About Prompt Engineering

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