Prompt engineering refers to the deliberate design and optimisation of inputs for AI systems. These inputs are called prompts and contain instructions, questions or information that an artificial intelligence (AI) is intended to process. The aim is to obtain results that are as precise, relevant and useful as possible. Through well-formulated prompts, users can influence which tasks an AI undertakes, what information it takes into account and how it structures its responses. Prompt engineering therefore plays an important role in the use of generative AI, for example in text generation, data analysis or software development.
How does prompt engineering work?
In prompt engineering, instructions are formulated in such a way that an AI system understands the desired task as clearly as possible. A good prompt may include the following elements:
- Task: What exactly is the AI supposed to do?
- Context: What background information does she need?
- Target audience: Who is the result intended for?
- Specifications: What requirements or restrictions apply?
- Output format: How should the result be displayed?
An example of a well-structured prompt
| component | Example |
|---|---|
| Task | Carry out a market analysis. |
| Context | Our start-up develops software for medium-sized businesses. |
| Target audience | The analysis is aimed at potential investors. |
| Requirements | Use clear language. Take market opportunities and risks into account. Indicate where data is missing. |
| Output format | Present the results clearly in a table. |
The more precisely the relevant requirements are described, the better the output can be tailored to the specific use case. However, a good prompt does not need to be particularly long. What matters most are clear, consistent instructions.
What is prompt engineering used for?
Prompt engineering is used wherever people work with generative AI or integrate AI functions into applications. Typical areas of application include:
- Marketing and communications: drafting texts, revising content and developing campaign ideas.
- Research and analysis: summarising information, evaluating data and organising findings.
- Product development: Developing concepts, writing programme code and testing software functions.
- Sales and Customer Service: Preparing customer enquiries, producing sales documentation and assisting with routine tasks.
- Process automation: Integrating AI into existing workflows and digital products.
For start-ups, Prompt Engineering offers the opportunity to make targeted use of AI applications and to streamline work processes. This can be particularly helpful when staff and financial resources are limited.
What methods are used in prompt engineering?
There are various methods for improving the quality of AI output:
- Clear instructions: Formulate tasks as specifically and unambiguously as possible.
- Provide context: Add relevant background information.
- Few-shot prompting: Showing the AI what the desired result should look like using just a few examples.
- Breaking tasks down: Breaking complex requirements down into manageable sub-steps.
- Define format specifications: Specify the desired length, structure or presentation format.
- Iterative prompting: Review results and improve prompts step by step.
Which method is most appropriate depends on the task and the AI system being used. It is often possible to combine several methods.
What mistakes should be avoided in prompt engineering?
Even well-formulated prompts do not guarantee error-free results. Users should therefore avoid common mistakes:
- Unclear or contradictory instructions
- Lack of background information
- Too many different tasks in a single prompt
- No details provided regarding the desired outcome
- Unverified reproduction of AI-generated content
- Entering confidential or personal data without appropriate safeguards
- The expectation that AI will reliably fill in missing facts
AI systems can generate incorrect, incomplete or fabricated information. This phenomenon is often referred to as AI hallucination. For this reason, important technical statements and decisions in particular must be verified. Prompt engineering improves collaboration with AI systems, but it is no substitute for expert knowledge or careful quality control.
How can innoWerft help with this?
innoWerft supports start-ups in refining their business models and identifying meaningful AI use cases. Through coaching and access to a network of experts and companies, founders can reflect on and further develop their AI-based ideas.
Frequently Asked Questions about Prompt Engineering
What is a prompt?
A prompt is an input to an AI system. It may consist of a question, an instruction or additional information, and determines which task the AI is to carry out.
Do you need programming skills for prompt engineering?
No, no programming knowledge is required for basic use. What is important is clear wording, a good understanding of the task and the ability to critically evaluate AI results.
What is the difference between prompting and prompt engineering?
‘Prompting’ refers to entering a command into an AI system. ‘Prompt engineering’ goes a step further and involves the systematic design, testing and refinement of such prompts.
Can prompt engineering improve the quality of AI responses?
Yes, clear instructions, appropriate contextual information and examples can improve the quality and relevance of AI outputs. However, the result also depends on the AI model used and the task at hand.
innoWerft is an early-stage investor and accelerator based in Walldorf, supporting start-ups before or shortly after they are founded by providing capital, coaching and access to a network of investors and businesses to foster sustainable growth. Would you like to develop your start-up further and find out more about innoWerft? Apply now for the Open Pitch.