Using AI Effectively: How Better Prompts Get You Much More from ChatGPT, Claude & Co.

In many companies, AI use follows the same pattern: an employee opens ChatGPT, types in a question, is disappointed by the result, and closes the window. Shortly after, a colleague asks the same thing — worded slightly differently, with a much better result. The AI is identical in both cases. Only the prompt is different.

This is not a minor detail. Those who can use AI tools precisely get consistent, usable results — those who ask ad hoc need many correction rounds or give up in frustration. This guide shows what the structure behind good prompts looks like, which mistakes occur most often, and how you as a company can systematically improve AI use — without requiring any technical background. For anyone also considering how AI integration and automation can flow into business processes, understanding good prompts is the indispensable first step.

What a prompt is — and why it determines everything

A prompt is any input you send to an AI model: a question, a task, a description, a combination of these. Models like ChatGPT, Claude, or Gemini have no memory beyond the current conversation and do not know your company. What they know and how they respond depends entirely on what you include in the prompt. The model is like a highly qualified employee on their first day: great abilities, but zero context about your processes, customers, and requirements.

The difference between a vague and a precise prompt is directly measurable. A vague prompt like → Write a sales text delivers something generic. A precise prompt — with role, target audience, tone, and length specification — delivers something usable. Both instructions come from the same wish; only one is clear enough for a workable result.

The four building blocks of a good prompt

Good prompts are not magic — they follow a recognisable structure. Anthropic's prompt engineering documentation and OpenAI's prompt engineering guide independently describe the same core elements. For everyday business use, four building blocks can be derived:

Building block 1: Role — who should the AI be?

Start with a role assignment: You are an experienced accountant, You are a copywriter for B2B technology companies, or You are a project manager in construction. This assignment steers the tone, depth, and perspective of the response more effectively than any additional instruction. The more concrete the role, the more fitting the result.

Building block 2: Context — what does the AI need to know?

Provide all information that a person would need to complete the task: who is the audience? What goal should the response fulfil? Which channel, which situation, what prior knowledge does the target audience have? Too little context leads to generic answers — too much does not hurt as long as the text stays structured.

Building block 3: Task — what should be produced?

Formulate the task as a concrete assignment, not a vague question. Write an email template for a payment reminder is better than How do you write payment reminders? If the result needs multiple variants, say so explicitly: Create three variants with different tones.

Building block 4: Format — how should the result look?

Specify the desired output format: prose, bullet list, table, key points, JSON. Name a length, e.g. maximum 200 words or 5 bullet points. Without this specification, the AI decides on its own — and the result rarely matches the intended use.

The five most common prompting mistakes

Before moving to refinement: these mistakes cost time daily in companies that already use AI.

• No context given: a prompt like Write an email without recipient, purpose, or tone — the result is inevitably generic and needs reworking.
• Multiple tasks at once: asking five questions in one prompt yields five shallow answers. One task per prompt delivers better depth.
• No length specification: without a format instruction the AI writes too much or too little — and rarely exactly what is needed.
• Deleting poor answers instead of refining them: often a short follow-up prompt suffices, e.g. That is too general — focus on mid-sized mechanical engineering companies with up to 100 employees.
• Entering confidential data: customer names, contract details, and salary figures do not belong in a standard ChatGPT prompt — more on this in the data protection section below.

Prompt types for everyday business use

Not every task needs the same prompt approach. The following overview shows the most important patterns that regularly arise in SMEs — with notes on which approach makes most sense when:

From the single query to a prompt library

Anyone who develops a good prompt today, starts the same task with a new prompt tomorrow, and phrases the same task differently the day after loses twice: time and consistency. An internal prompt library — even a simple shared document — solves this problem. Good prompts are developed once, tested, and stored so everyone can access them.

An analysis by erhardt-itservices.de concludes that medium-sized companies should train prompting internally as early as possible: employees who can formulate precisely need significantly fewer correction rounds and achieve better results with the same systems. The daily time savings typically amortise a brief internal training session within a few weeks. If you want to involve structured IT consulting and technology assessment, this step can be tailored specifically to your existing tools and processes.

• Start with 3–5 tasks your team solves via AI daily (e.g. emails, meeting minutes, summaries, sales texts).
• Develop a baseline prompt for each task using the four-building-block principle — test it with real inputs, compare 2–3 variants.
• Store the tested prompts in a shared document (e.g. Notion, Confluence, SharePoint) — that is already the core of a prompt library.
• Designate one person to maintain the library — this does not need to be an IT person; a tech-savvy employee from marketing, HR, or sales is sufficient.
• Review the library quarterly: new model versions continuously improve, and what was necessary six months ago may no longer be needed.

Data protection and security when prompting

AI tools like ChatGPT Free and ChatGPT Plus use conversations for model training by default. This means: what you enter can in principle flow into the further development of the model. For companies working with customer, contract, or personnel data, this is a concrete data protection issue.

• No personal data in standard prompts: names, addresses, contract details, and salary figures do not belong in a prompt — not even loosely anonymised if the context still allows inferences.
• Check business plans: ChatGPT Team and Enterprise as well as Anthropic's Claude for Business offer training opt-out and a Data Processing Agreement — this is the minimum requirement for use with company data.
• Consider API instead of consumer interface: those who integrate AI via API get no training opt-in by default and can contractually govern data processing.
• Create an internal policy: a brief orientation on what is and is not allowed in AI tools prevents the most common unintentional data incidents.
• Observe the EU AI Act: since August 2026 the full requirements of the EU AI Act apply to high-risk systems. AI use in HR processes, credit decisions, or safety-relevant areas requires documented risk assessments.

How to get started concretely now

The most common mistake when building AI competency in a company is trying to tackle everything at once. A pragmatic entry sequence that has proven itself in practice:

1. 1Choose one use case: which task costs your team the most time with the worst AI results? Start there.
2. 2Develop a baseline prompt: apply the four building blocks, test it, compare 2–3 variants. This typically takes 30–60 minutes.
3. 3Store and share the prompt: make this one good prompt accessible to everyone — that is already the core of a prompt library.
4. 4Repeat: next task, next prompt — after 4–6 weeks your team has a functioning foundation and significantly less frustration when working with AI tools.

If you are also thinking about how AI can be integrated more deeply into your business processes — as an automated workflow, as an assistant in existing systems, or as an interface to your data — our team can support you with AI integration and the right technology entry point. It does not start with a large project but with the right pilot and the experience of what actually works in comparable companies.

A good prompt is not a trick — it is a clearly formulated brief. Those who give their AI as much context as they would an experienced employee also get comparably good results.

AI integration for the SME sector — NoviCogi

Frequently asked questions

What is prompt engineering — and do I need it as an SME?

Prompt engineering refers to the deliberate formulation of instructions to an AI model in order to obtain consistent and useful results. For SMEs this requires no programming or technical knowledge — just knowing how to give an AI as precise instructions as you would a good employee: with role, context, task, and format. Even simple improvements such as a role assignment and a length specification make a noticeable difference in daily use.

Which AI tools are best suited for SMEs?

The most common entry-level tools are ChatGPT (OpenAI), Claude (Anthropic), and Gemini (Google). All three are accessible via browser, have free entry plans, and deliver very good results for texts, analyses, and summaries. For companies with data protection requirements, business plans are necessary — ChatGPT Team and Claude for Business disable model training with your data by default.

How do I prevent the AI from inventing facts?

AI models can invent information that sounds plausible but is wrong — this is called hallucination. Countermeasures: never rely on AI alone for numbers, dates, and facts — verify the source. Explicitly request citations and check them. Use AI for phrasing and structure, not for research you cannot cross-check. Models with integrated web search significantly reduce the problem for current information.

How long does it take a team to truly master prompting?

The basic principles — role, context, task, format — can be understood and applied within half a day. For consistent results in everyday tasks, 2–4 weeks of active use with regular team exchange is typically sufficient. A half-day internal training session significantly accelerates the process and prevents bad habits that would otherwise develop.

Can I enter company data into ChatGPT?

It depends on the plan. ChatGPT Free and Plus conversations are used for model training by default. ChatGPT Team and Enterprise disable training by default and offer a Data Processing Agreement for GDPR-compliant use. Personal data, contract details, or salary information should under no circumstances be processed unprotected via external AI services without an appropriate contractual basis and data protection review.

What is the difference between an AI chat and an AI agent?

In a chat, the human initiates every request and the AI responds — and that is it. An AI agent receives an overarching goal and decides on its own which steps to take: reading files, executing actions, correcting errors, combining multiple tools. Chat prompting is the entry point; AI agents are the next level of automation. More on this in the article on AI levels: from ChatGPT to agents to automated workflows.

This article was created with AI assistance and editorially reviewed.