Effective prompt engineering: The key skill for generative AI in management systems

What is prompt engineering?

Prompt engineering is a discipline that aims to use targeted instructions to solve complex problems of AI models as effectively as possible. In simple terms, a prompt is the input from the AI user, on the basis of which the AI generates an output. Especially in the area of management systems, this approach opens up new opportunities for collaboration between humans and machines. The use of targeted prompts to process complex tasks could usher in a new era of working, in which manual and time-consuming tasks can be replaced by intelligently formulated inputs. The aim of a message is to make the requests as efficient as possible in order to achieve the desired results in the best possible way.

Make prompts effective

Designing effective prompts requires no previous technical knowledge. One approach to structuring a good prompt is to divide the prompt into four sections: instruction, input data, context, and output indicator. This structure enables the user to provide the AI with the necessary information so that it is highly likely to effectively solve the given task. Depending on the complexity of the task, individual components can be omitted.

Instruction — The task that the model should solve

The instruction defines how the model should behave. It provides the framework within which the model can move.

• Example: You are a process manager and have the task of creating processes. The processes that you create are used in a medium-sized manufacturing company with 400 employees. The process descriptions should be as detailed as possible, but not too comprehensive so that they can serve as support for users in everyday life.

Input data — the inputs for which a solution is to be found

The input data define the task for which a solution is to be found.

• Example: Create a process to identify, control, and manage risks across the organization.

Context — more information that guides the model

The Context section provides additional information to help and guide the model in finding solutions.

• example: The risks should be identified by all employees and be able to be managed by the risk manager via our risk management software. We differentiate corporate risks from process risks. Corporate risks have a direct impact on business continuity when they occur and process risks only affect the defined process goal when they occur. After a risk has been identified, it is assessed by the risk manager according to our risk scheme in the categories meaning, probability of occurrence and probability of discovery and described in more detail for the quarterly risk round. Each quarter, managers then meet, reassess risks, define measures to reduce risk, and review measures defined in the previous risk round. In this way, we want to continuously reduce and keep an eye on our risks.

Output indicator—the format of the output

In the Output Indicator section, you can also define in which format the model should provide us with the result.

• example: The result should be a process with at least 8 steps. Each process step is enriched with detailed information that explains this step in more detail. The process should be output in tabular form, with the process steps numbered in ascending order.

Structure alone is not enough. The formulation of the prompt also strongly influences the results. A good prompt should be structured, specific, relevant, humane and iteratively formulated to achieve optimal results.

• Structured: It helps the AI to break down large and complex tasks into subtasks and to formulate them in sections
• specific: The more helpful details you can provide in the prompt, the better the expected results will be. You can consciously use this to make AI more creative by deliberately omitting known information and analyzing the result.
• Relevant: Many details are good. However, these details should also be helpful and help solve the core problem. Since AI is trained by people, AI also has a certain amount of “attention.” If too much information is given, the AI automatically sorts out and omits potentially important details and focuses on less important ones.
• Humanly: AI is trained by people, meaning it understands the input better the more humanely it is described.
• iteratively: You should be aware that “prompt engineering” is often an iterative process. Improving the prompt based on the result and consciously managing the AI leads to better results.

Once these tips and rules have been internalized, the next step is to start building recursive prompts. This is a tool that the AI uses to iteratively improve its own results by making suggestions to the user for better inputs and asking questions that would improve the outcome. The user is interviewed by the AI and the AI thus improves its own results.

Prompt Engineering Challenges

The possible uses of language models and prompt engineering seem almost unlimited. However, certain precautionary measures must be taken when using Prompt Engineering

• data security: A key point when using Prompt Engineering is the protection of personal data. Both companies and individuals must ensure that personal information remains confidential and is not misused. One solution to ensure data security could, for example, be the local execution of language models so that data does not have to be passed on to external providers.
• hallucination: In addition, language models are often regarded as a “black box”, as AI decision-making processes are not completely transparent. This is reinforced by the lack of reproducibility of AI decisions, as generative language models generate their answers anew based on previously trained data, which makes it difficult to evaluate these answers. That is why you should not blindly trust these models, but carry out external reviews for a correct evaluation. If the AI is unable to find an answer based on its training data, incorrect outputs, so-called “hallucinations”, can occur.
• Bias: In addition to technical and safety-related issues, the aspect of bias must not be ignored. It is essential to consider that models make decisions based on their training data, which can lead to discriminatory results if this data is distributed unevenly.

The future starts now: Prompt engineering and AI-driven management systems

Overall, Prompt Engineering is at the center of an exciting development that has the potential to fundamentally change the way we use and optimize management systems. By thoroughly studying this discipline, companies and individuals can take full advantage of the benefits of generative AI and develop novel solutions for complex challenges.

The future of AI in management systems promises a drastic cost reduction in the life cycle of requirements and a more efficient design of processes. Presentations and modules in the context of management systems offer further insights and opportunities for integrating AI technologies.

Prompt Power - 6 prompts to try out

Ready to take your processes to the next level? Start right now and see for yourself what's possible! We have prepared 6 cool prompts exclusively for you, with which you can start and experiment right away.

Our goal is to provide you with high-quality information that helps you to clearly document and optimize your processes and always keep them up to date. With the six prompts, we go through various areas of process optimization, from documentation and planning to implementation and adjustment to international standards such as ISO 9001.