Prompting Guide for companies
How to get better AI results with clear prompts
The employees in your company are certainly using AI, but somewhere between an experiment and an everyday trick. Individual employees open ChatGPT, Gemini or Copilot on the side to write emails faster, condense evaluations or summarize long documents.
Some days the results are impressive. On other days, you ask yourself: “What did the AI understand?”
At the latest then the same question arises: Is it the AI or our prompts?
This is exactly where a professional prompting guide comes in. It transforms “we try things out a bit” into a clear way of working: defined roles, reusable prompt structures, understandable examples and comprehensible rules for language, data and risks.
This article provides you with a practical guide on how to set up prompts in your company so that your teams can work reliably with generative AI. From the basics and advanced techniques to standards and governance.
5 key takeaways
- Prompting is not a “nice-to-have” in the company, but a core competence: those who prompt properly get better results, save time and reduce frustration.
- A clear prompting guide turns individual experiments into a standard: roles, structure, examples and do’s & don’ts are defined once and used by everyone.
- Two basic modes are sufficient to get started: concrete prompting (clear task, recurring use cases) and explorative prompting (joint reflection with the AI).
- Advanced techniques such as chain-of-thought, reflection or tree-of-thought prompting improve quality – especially for complex decisions, analyses and concepts.
- Prompting belongs in AI governance: data rules, documentation and AI Act compliance should be included in the prompting guide so that efficiency and compliance go hand in hand.
Good prompting in the company - why the topic is important now
Prompting is essentially nothing more than conducting a conversation with an AI. With each prompt, you define the role of the AI, the context it knows, the task it should solve and the result you expect. In the end, the output is only as good as this input.
However, as long as every person in the company types “freehand”, the result remains a matter of luck: one colleague writes three paragraphs of context, the colleague next to them only types half a sentence. One manager demands a clearly structured table, the next person a loose free text. And topics such as data protection, the AI Act or internal guidelines are often only considered when someone has a “funny feeling”.
As a result, there are no repeatable, scalable processes. Instead of a genuine AI competence in the company, you have many individual chats – without a common standard.
A company prompting guide addresses precisely this problem. It creates a common understanding,
- how good prompts are structured,
- provides reusable templates for typical tasks,
- Defines clear do’s and don’ts for data, language and handling results
- and offers starting points for governance and AI Act-compliant use of generative AI.
Professionalize AI prompts
What is prompting and what makes a good prompt?
Prompting describes the deliberate formulation of inputs to generative AI models such as ChatGPT, Gemini or image models such as Midjourney, with the aim of obtaining a concrete result. Not “a bit of text”, but a result that fits the task, target group and context.
A good prompt…
- Clearly describes the role in which the AI should act
- provides concrete context for the order
- Defines goal and success criteria
- defines format, length and tone
- delimits what should not happen
The difference quickly becomes apparent.
Instead of:
“Write me something about our new product.”
Lieber:
“Act as a technical editor. We are launching a new B2B SaaS product for temporary employment agencies. The aim is to write a comprehensible, factual product description for our website. Explain in 3-4 paragraphs what problem we solve, what the main functions are and what benefits the solution has for dispatchers in their everyday work. Write in German in simple, clear language.”
The AI is the same, the model is the same: the difference lies in the structure of the input. It is precisely this structure that you can standardize within the company: as a recurring pattern that teams can adapt for different use cases, but do not have to reinvent each time.
Two basic strategies in prompting: concrete vs. explorative
In day-to-day business, two types of prompt have emerged, depending on how clear the goal is: either you know pretty much exactly what the end result should be. Or you first want to let the AI “think along”.
Concrete procedure - if you know what you need
In this mode, you treat the AI like a very clearly briefed service provider: the task is known and the result should be as stable and reproducible as possible.
Typical examples are
- recurring e-mails
- Minutes and summaries
- Standard texts such as product descriptions or FAQ drafts
- First drafts for presentations or reports
It is worth having a fixed template that you can use again and again. For example:
- Role:“Act as [role, e.g. specialist journalist for B2B IT].”
- Context:“I’ll explain what we’re planning: [background, target group, framework conditions].”
- Objective:“Your task is: [specific task, e.g. article draft, e-mail, analysis].”
- Format & scope:“Create [number of paragraphs/pages, bulleted lists yes/no, tonality, language].”
- Quality criteria:“Particularly important is: [e.g. clarity, no technical terms without explanation, reference to ROI].”
This structure can be recorded in the company as a “standard prompt” and varied slightly depending on the area. In this way, a shared library is gradually created from individual good ideas.
Explorative approach - if you want to think along with the AI
Sometimes the task is not yet really tangible. It is then more about understanding options, sorting a topic or developing hypotheses. In such situations, AI becomes a sparring partner.
An exploratory prompt could look like this, for example:
“Act as [role, e.g. strategy consultant for medium-sized industrial companies]. I’ll briefly explain the context: [initial situation]. What 5-7 sensible steps would you suggest to achieve [goal]? Explain each step in 2-3 sentences and suggest specific questions that I should ask you in the next rounds.”
In this way, you deliberately open up the space for options instead of directly asking for a finished result.
The explorative approach is particularly suitable for market and competition analyses, idea generation, process design or the preparation of workshops – wherever clarity about the problem must first be created before concrete texts or artifacts are produced.
The building blocks of a strong prompt: a structure suitable for the company
A good prompt is not a product of chance, but a clean structure. From a company perspective, it is worth explicitly naming the most important building blocks and translating them into reusable templates.
Six elements have proven themselves in practice:
- Role (“Act as …”)
What perspective should the AI take? Expert, editor, lawyer, project manager? - Context & background
What exactly is it about? Project, target group, industry, framework conditions – everything that helps to categorize the task. - Goal & success criteria
What is a good result? Summary, decision template, e-mail draft, list of options? - Format, style, scope
What should the result look like? Number of paragraphs, key points yes/no, tone of voice (factual, sober, promotional), language. - Examples (Few-shot)
If available: one or two sample texts or structures to guide the AI. - Boundaries & data rules
What is taboo? Which sources may be used, which content may not be invented, which data is sensitive?
The more consciously these points are addressed, the easier it is to reuse, document and gradually improve prompts within the company, for example as an internal “Prompting Deutsch” manual or compact cheat sheet.
A company prompt could then look like this:
“Act as an experienced project manager in mechanical engineering.
Context: We are preparing a steering committee update for a digitalization project, the target group is management and divisional production management.
Objective: Create a concise summary for a PowerPoint slide with status (traffic light), key results of the last 4 weeks, risks and decisions that the steering committee must make.
Format: maximum 120 words, clear, factual wording, no buzzwords.
Limits: Use only the following key points as a basis [insert key points] and do not make up facts.”
Such patterns are ideal for a central prompting guide on the intranet or in your AI governance documentation and they turn “everyone writes something” into a repeatable standard.
Best practices for good prompting in everyday life
Many “prompting hacks” can ultimately be broken down into a few simple principles: clear, friendly, concrete and step-by-step instead of one-shot.
Clarity and precision
Context instead of guesswork
The AI does not work “by feel”, but with what you enter. If the target group, channel, tone of voice or internal framework conditions are important, they also belong in the prompt – or in linked documents to which you explicitly refer. The less the AI has to guess, the better the output.
Simple language
Setting up a conversation
Advanced prompting techniques for teams
As soon as the basics are in place, it gets exciting: then it’s no longer just about getting “somehow good answers”, but systematically better ones – especially for complex tasks, decisions or analyses. This is where advanced prompting techniques come into play.
Chain-of-thought prompting
Instead of just asking for the result, ask the AI to think visibly step by step. A typical prompt would be:
“Please solve the task step by step. First write down your thoughts in clearly numbered steps and only formulate a compact conclusion at the end.”
This increases transparency and makes it much easier to recognize errors, weak arguments or misunderstandings – especially when several people continue to work with the result.
Reflection prompting
Here you let the AI take another critical look at its own answer:
“Critically evaluate your last answer: Where could there be errors or gaps? Which assumptions are uncertain? Then create an improved version.”
This creates a kind of built-in quality loop. You do not have to check every detail manually, but receive a second, reflected version.
Tree-of-thoughts prompting
Instead of pursuing just one path of thought, the AI explores several options in parallel. For example:
“Propose three different approaches. Briefly analyze the strengths and weaknesses of each approach. Then make a well-founded decision on the best approach and formulate a concrete plan in 5 steps.”
This is particularly helpful when making strategy, architecture or risk decisions: you not only see the result, but also the alternatives that were rejected along the way.
Few-shot prompting
If the format is particularly important, such as the style of FAQ answers, the structure of meeting minutes or the tone of a particular brand, 1-3 examples will help. You give the AI small “sample pieces” that it can use as a guide. The structure and tone are adopted without you having to re-explain every detail.
Creativity-Prompting
Sometimes you need to deliberately break with routine. Then you can deliberately push the AI off the beaten track:
“Act as a creative concept developer. Develop 5 unconventional ideas that deliberately go against our previous approaches. Briefly explain why the idea could still work.”
This opens up space for experimentation while still having a clear task.
AI agents
“AI agents” can now be defined in many tools: predefined roles with fixed prompts, documents and limits. Instead of starting from scratch every time, your employees then select the “Sales Assistant”, “Research Analyst” or “Policy Checker”, for example – each of which is based on a sophisticated prompt configuration.
The company prompting guide should clearly document which agents exist, what they can be used for and who maintains them. This turns individual good prompts into a genuine infrastructure for AI-supported collaboration.
Prompting in the corporate context: standards, risks, governance
Prompting is not only an efficiency lever, but also always a governance issue. At the latest with the EU AI Act and stricter internal guidelines, it is no longer enough to “try out a little”. Companies must answer clearly: How can AI be used? And what content has no place in prompts?
Three guard rails are central:
First: Confidential data. Personal data, health information, confidential contract details or unpublished financial figures do not belong uncontrolled in external AI models. Clear rules are needed here as to which systems may be used and how data is protected.
Secondly: Transparency. Employees should understand that AI content is not “magically correct”, but is generated statistically. Errors, distortions or hallucinations are part of the system and must be classified as such. This also means that important content is proofread, not blindly accepted.
Thirdly: Documentation. Where AI results are incorporated into critical processes – such as risk analyses, compliance recommendations or strategic decisions – prompts and results should be documented. Not as a bureaucratic monster, but in a way that ensures traceability and auditability.
A good prompting guide is therefore always also a governance tool. It not only describes how to write good prompts, but also what is permissible, what data is taboo and where human review remains mandatory. This turns “AI in everyday life” into a responsible, auditable practice.
Interim solution for AI needed?
Your company prompt standard in 5 steps
How do you move from individual AI experiments to a lived, common prompt standard? The best way is step by step. Without a major project, but with a clear direction.
1. prioritize use cases
Start where a lot of text work is already being done today and where AI is being used “secretly” anyway: Emails, logs, FAQ drafts, standard reports, initial analyses.
These tasks are ideal candidates: clearly defined, frequently recurring, time-consuming. If you establish clear prompts here, teams will notice the effect very quickly.
2. define prompt templates (concrete & explorative)
It is worth creating two variants for each prioritized task:
- a specific version for recurring tasks (e.g. project status mail, meeting minutes)
- an explorative version if you need the AI as a sparring partner (e.g. structure for a new strategy workshop)
Both variants end up in the joint prompting guide – with a brief explanation, typical application scenario and a few do’s and don’ts. This is how “good individual prompts” slowly become standards.
3. set up internal prompt library & versioning
Instead of prompts “disappearing” in private chats, there needs to be a visible place:
- an area on the intranet or in Confluence
- or a shared area in the AI tool used
The structure is more important than the tool: Who created the prompt? For which use case is it intended? Which version is current? With comments and versioning, “that one good prompt from colleague X” becomes a resource for entire teams.
4. training & enablement (academy, guidelines, examples)
A prompting guide only works when people work with it.
This means: short-format training courses, internal walkthroughs (“this is how we use this prompt in everyday life”), small nugget formats on the intranet and perhaps also “prompt clinics” in which teams sharpen their prompts together.
Prompting is therefore not perceived as an additional task, but as a tool that makes everyday life noticeably easier.
5. measure and improve quality
A simple feedback system is needed to ensure that the whole thing does not remain a static document. Criteria can be, for example
- how much time the task actually saves with AI support
- how specialist managers assess the quality of the results
- how often incorrect figures, inappropriate tonality or factual errors occur
On this basis, prompts can be sharpened in a targeted manner – for example with the help of reflection or tree-of-thought techniques. In this way, your prompting guide improves with every iteration and adapts to the reality of your teams.
5 common prompting mistakes and how to avoid them
We encounter a few pitfalls in almost every company, regardless of industry or tool. The good news is that they are relatively easy to defuse.
Too little context
Better: Always include the goal, target group and channel, for example: “LinkedIn post for decision-makers in SMEs, goal: awareness for our new whitepaper, tone: factual but accessible.”
Too many tasks at once
For the AI, this is a bit like calling out four tasks at once to a person in the hallway. Better to proceed step by step and prompt each task separately, ideally based on the same context block.
No clear boundaries
Better: Name no-goes directly in the prompt (“Avoid legal assessments”, “No promises on delivery times”, “Do not mention confidential project names”).
No iteration
Better: let the AI deliberately sharpen up. For example, with instructions such as “formulate more factually”, “add risks”, “reduce to 150 words” or “adapt the tone to our target group CFO”.
No data rules
This is exactly where your Prompting Guide in combination with AI governance and AI act compliance helps: clear examples of what is allowed, what is taboo and which tools may be used for sensitive data. This turns “I hope it fits” into a comprehensible standard.
Get your teams fit for prompting
Conclusion
Prompting is much more than a “trick” to get prettier text out of ChatGPT. In the corporate context, it is a real key competence. Those who consciously structure and standardize prompts and embed them in their own AI governance increase the quality of the results, reduce risks and create the basis for a measurable ROI.
With a clear prompting guide
- your teams speak a common “AI language”,
- Use generative AI consciously instead of randomly
- and turn individual experiments into a scalable component of your AI strategy.
Step by step, AI playfulness becomes a reliable working tool and prompts become part of your company-wide expertise, not just individual power users.
FAQ
Many employees already use ChatGPT, Copilot or Gemini – but each person prompts differently. Without a common standard, results remain random, difficult to reproduce and sometimes risky (keyword: data). A prompting guide creates clear structures, reusable templates and rules for language, data and quality. This turns “we’ll try it out” into a scalable, controllable use of AI in everyday life.
Good prompting follows a structure: role, context, goal, format, boundaries. A weak prompt is “Write me something about our new product”. A good prompt defines the role, target group, tone, scope and no-gos. This increases relevance, reduces rework and makes results repeatable. In the Prompting Guide, this structure is clearly described once and backed up with examples.
A structured prompting guide reduces the time required per task (e.g. for emails, logs, evaluations), increases the quality of AI results and reduces error and compliance risks. This has a direct impact on productivity and indirectly on faster decisions. Above all, you build up company-wide expertise instead of being dependent on a few “power users”.
Instead of writing a big concept, we recommend a pragmatic start in five steps:
- Prioritize 3-5 recurring use cases (e.g. logs, emails, summaries)
- Define one concrete and one explorative prompt template for each use case
- Create a simple prompt library in the intranet or AI tool
- Set up short training and nugget formats for the teams
- Collect feedback on quality and time savings and iteratively improve the templates
A comprehensive prompting guide can then be created from this basis, which will later be embedded in your AI and governance strategy.
Before starting a project, you should clarify three things: firstly, clear goals and use cases (which requests should be automated), secondly, a reliable knowledge base with up-to-date content and thirdly, framework conditions for data protection, security and responsibilities. On this basis, you can decide which type of chatbot and which operating model (e.g. European cloud) suits your company.
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