Learn prompt engineering
Five techniques, one framework and concrete examples show how you can learn prompt engineering.
What is prompt engineering?
Prompt engineering is the structured method of designing and optimising input instructions (prompts) for generative AI models in order to obtain precise, relevant and high-quality results.
It is a mixture of creative writing, logical thinking and technical understanding in order to steer AI systems effectively instead of just asking vague questions.
The technical basis is provided by NLP (natural language processing) – the field that determines how language models break down and interpret text.
Method
How does the prompt engineering framework work?
The framework uses five fixed building blocks so that you get reproducible results: task, context, role, result and limit.
Task
Describes precisely what the AI does.
Context
Explains the background so that the answer fits the topic.
Role
Influences which words the AI uses and how deep it goes into detail.
Result
Chooses whether the AI writes a table, JSON or three sentences.
Limit
Prohibits jargon or sets a limit of 200 words.
Technique 1
How do you formulate clear instructions for AI models?
In prompt engineering, AI models need clear instructions: task, context, format and target audience.
- If one of these parts is missing, the model writes texts that sound too general and do not help the user
- Anyone who names the target audience gets texts whose tone and depth fit the reader
- Negative instructions such as “Do not use jargon” or “Do not write more than 100 words” narrow the answer down
Technique 2
How do I work with examples in my prompts?
You give the AI one or two finished text examples as a template so that it adopts style, tone and format – this is called few-shot prompting.
Before
Write product descriptions for our shop.
After
Write a text for Aceto Balsamico (250 ml). Use this style: “Cold-pressed from Koroneiki olives. Fruity and tart with a peppery finish. Goes with salad and pasta.”
Technique 3
How do I get AI to think step by step?
The instruction “Think step by step” makes the AI show its path to the solution logically.
. This prompt engineering technique helps especially when you analyse data, compare things or create plans.
Before
Which channel brings in the most at €50,000?
After
Analyse the results for €50,000 per year. . Think step by step: 1) List channels. 2) Estimate cost per lead. 3) Check whether the channel can be scaled. 4) Recommend a path and justify it.
Technique 4
How do I assign the right role to AI models?
Anyone who starts with “You are an experienced [profession]” determines which words the AI chooses and how deep it goes into the topic.
Role prompting tailors the answer exactly to what the target audience expects.
Before
Write an email about the prices.
After
You work in customer service. Write to a customer who has been using us for two years. Our prices rise on 1 July by 12%. Write honestly and appreciatively. Do not use empty phrases.
Technique 5
How do you define the format of the answer?
Anyone who defines the output format in the prompt determines how long the text is, how it is structured and in what form the AI answers.
Length
“Maximum 150 words”, “Three sentences” or “One paragraph”.
Structure
“As a list”, “With headings” or “In a table”.
Data format
“Output the result as JSON” or “Create a CSV file”.
Tools
Which tools are suitable for learning prompt engineering?
The AI models of the major providers are suitable for learning prompt engineering – each with its own strengths:
ChatGPT
Offers the broadest user base and many community templates to use as a starting point.
Claude
Particularly suitable for long texts, analyses and instructions with many steps.
Google Gemini
Accesses Google services directly and fits well if you work with Sheets, Docs or Gmail.
Practice
How do you build your own prompt library?
You build your own prompt library by turning proven instructions into modular templates with fixed variables and managing them systematically in a central, categorised database.
Modular structure
Every prompt needs a fixed scaffold of persona, context, variables (placeholders) and precise exclusion criteria in order to deliver reproducible results regardless of who uses it.
Systematic filing
Store the prompts in categories and areas of application such as SEO, marketing and sales in tools such as Notion, Google Drive, Obsidian or GitHub.
Versioning and maintenance
Because AI models keep evolving, you should regularly check prompts for quality, reliability and token costs.
FAQ
Frequently asked questions about prompt engineering
In Germany, prompt engineers earn between €55,000 and €90,000 gross per year.
Freelancers charge between €800 and €1,500.
No, you can learn prompt engineering without a formal qualification by taking courses and practising a lot with different models.
Anyone who proceeds methodically does not need a certificate to achieve good results.
Google Cloud, AWS and the Haufe Akademie offer courses that end with a certificate.
Certificates from Google or AWS count for more in a job interview than papers from unknown companies.
In some German federal states, employees can take educational leave if the AI training is officially recognised.
The IHK chambers of commerce or adult education centres offer such courses, for which you get time off.
Anyone who practises for two to four weeks understands the basics.
Those who use the techniques every day at work learn much faster – an AI training shortens the path.
Prompting means simply asking the AI a question.
Prompt engineering uses methods to steer the answer precisely.
That way you get a result every time that you use directly.
A prompt engineer thinks analytically, writes precisely and tests until the large language model answers perfectly.
You do not have to be able to program, but it helps in everyday work.
Anthropic, LearnPrompting.org and Google offer free courses and texts.
You learn best when you use AI every day for tasks.
Transparency
Sources used
- Own research
- OpenAI: “Prompt Engineering Guide” (2025)
- Anthropic: “Prompt Engineering Overview” (2025)
- Google: “Prompt Engineering Whitepaper” by Lee Boonstra (2025)