Why exact Prompting makes the Difference
If you ask inaccurate questions, you will get inaccurate answers. This applies just as much when talking to people as it does with AI. Customers describe answers as “too superficial, too imprecise, not helpful,” but the reason is often not the AI itself, but the nature of the request. Poor prompting = poor results. But what exactly is prompting?
Prompting is generally the way in which inputs, i.e. prompts, are formulated to artificial intelligence.
Good prompting turns AI into a real assistant that answers precisely and contextually. Requests can be just a question, but they can also be complex scenarios and requests. In general, the more specific the prompt, the more detailed the AI's response. Accordingly, the positive effects of successful prompts can be derived:
- Relevance: Users perceive the answers as tailored to their information needs
- Efficiency: Requests and corrections are reduced, work processes are more efficient
- User Satisfaction: Trust based on precise answers improves interaction with the AI application and the company that uses it.
Prompting is therefore not just about technical requirements, but about the communication interface between human and machine.
What is important when it comes to prompting?
How can this interface between AI and users be designed in the best possible way? Important factors include the choice of prompt style, the clear structuring of inputs, the use of suitable formats and instructions, and the provision of relevant contextual information so that the AI correctly understands and implements tasks.
Prompting types
The three most commonly known types of prompting are Single-Shot, Zero-Shot and Few-Shot. A comparison:
An unspecific prompt such as “Explain AI to me” only provides a general definition without going into much detail about the context or view of the request. On the other hand, a detailed request, such as “Explain to me in three concise sentences how AI can automate recurring questions in customer service,” results in an output of the AI that is tailored to the context of the request and takes into account the individual content, in this case the practice-related application.
Core properties of prompting
For writing the prompt, the command prompt, there are four core areas to consider: persona, task, context, and format.
The persona describes which role or perspective is handed to the AI (e.g. “I am a developer.”). A clearly defined persona helps to get more relevant and context-specific answers. The task, i.e. the specific instructions for the AI, is then described. Here it is important to formulate specifically and concisely. The more relevant details are included, the more targeted and useful the answer becomes. Finally, the format of the output can be determined based on the form information. This could be, for example, an email, list, spreadsheet, or summary as text. These factors form the basis of high-quality AI prompts.
Tips from OpenAI
OpenAI also provides a remedy for creating prompts. It is one of the leading companies for AI research and practical application and stands behind ChatGPT, the language models that are used in chatbots and assistance systems worldwide. The company puts online Best Practices , which have emerged from various projects and show how to achieve significantly better results based on structured inputs. Here are the most important findings from the article as tips:
- The latest model: that The latest and most powerful model is usually easier to control and provides better results.
- Instructions to start with: If the prompt starts with clear instructions, separated from the context, misunderstandings can be immediately avoided.
Example: Summarize the following text as a list. Text: “...”
- Specific and detailed: Precise instructions on context, target outcome, format, style and length make it possible to achieve the desired results.
Example: Instead of “Write a text about AI” better “Write an inspiring text about AI in newsletter format that inspires readers and arouses curiosity.”
- Formatting examples: Using examples of the form of structuring, the model can learn how the output should be structured. This makes it easier to interpret and process the results.
- From zero-shot and few-shot prompting to fine-tuning: If simple prompts provide inadequate results, examples are then added or, if necessary, specific adjustments are made to the model by fine-tuning the model.
- Reduce vague and inaccurate descriptions: Unclear instructions such as “Explain this simply” should be avoided; instead, phrases such as “Explain this in simple terms for a beginner” are more decisive.
Example: Instead of “The description for the AI product should be relatively short, just a few sentences and not much more.” A detailed instruction is recommended:”Describe the AI product in a paragraph of 3 to 5 sentences.”
- Positive, not negative prompts: Instead of saying what should be avoided, it is better to incorporate clear instructions for action. Labeling unwanted behavior can be misleading.
- “Leading words” for code generation: When it comes to coding, it is particularly important to give specific instructions. Introductory words about the desired target language, e.g. “Write a simple Python function that...” help the model understand and implement the desired code style.
These best practices provide a guide to improve communication with AI.
Which Model for which Purpose?
Prompting engineering, i.e. designing and testing the prompts, is usually carried out via an interface (API) that interacts with the LLM. As a result, the functions of the LLM are used efficiently, but above all, the capacity of the LLM is also improved. The language model of the AI is the system that has been trained to understand and generate human language. In the context of LLM, such as GPT, a token is a “component” of the language that the model processes. Language models count inputs and outputs in tokens. Depending on the model, the billing is done accordingly. Here are the most important OpenAI models at a glance:
GPT-3.5
GPT-3.5 is an evolution of GPT-3 and was presented in November 2022 together with the ChatGPT application, followed by several Turbo API versions with gradual improvements. The model creates human-like text, translates content and answers questions in a contextual way. It's good for everyday tasks, but it's less powerful than newer versions. Overall, it requires significantly less computing resources in comparison.
GPT-4
GPT-4 was released in March 2023, as well as following turbo versions later on. It works more reliably and creatively than GPT-3.5 and supports multi-modality so that both texts and images can be processed. The model has a significantly larger context window, which means that it can now take into account and process up to 128,000 words of input instead of 16,000 words previously in GPT-3.5. GPT-4 was trained on larger and diverse data sets, which allows it to better handle complex queries and even learn writing styles from users. According to OpenAI, it achieves 40% higher factual accuracy, but is slower to process. As also for 3.5, OpenAI has not published a specific risk rating for GPT-4. These ratings reflect the potential risks associated with using each model and are intended to alert developers and users to potential threats. They are published for all recent models.
GPT-4o
As a multimodal model, GPT-4o processes text, audio, image and video in prompts and supports fine tuning so that developers can adapt the model to specific use cases. Thanks to efficient token processing, it is less expensive to use, yet OpenAI classifies GPT-4o as a medium-risk model due to increased hallucinations, data security concerns, and even the risk of unintentionally reproducing and creating copyrighted content. OpenAI rated GPT-4o as a medium risk based on the highest risk score in the Persuasion category. This means that the model is able to produce compelling content, which could be problematic in certain contexts (e.g. by unintentionally reproducing copyrighted content).
GPT-5
According to OpenAI, GPT-5 is considered the most powerful model to date and forms the basis of ChatGPT today. It combines several models, with an intelligent router deciding in real time whether to use GPT-5, GPT-5 Thinking or a mini version, depending on the complexity of the request and the users' intentions. OpenAI has rated GPT-5 as a high risk because the model has an increased ability to create deceptions, which can be problematic in safety-critical contexts (particularly in the CBRN category chemical, biological, radiological, nuclear). The model opens up new opportunities for developers, but therefore requires careful handling with defined security barriers.
An Overview of all OpenAI language models can be found on the website.
Other Models
In addition to the GPT models, there are numerous AI language models with different strengths. Llaude by Anthropic was developed for secure and transparent interactions and places particular emphasis on ethical and comprehensible answers. Cohere offers powerful models for word processing and analysis, which are particularly appreciated in business practice. LLaMA by Meta is particularly popular in research and open source projects. Gemini from Google combines advanced language and knowledge processing and aims for precise, context-based answers in diverse applications. Google Gemini is currently considered the second-best-known AI model after OpenAIS ChatGPT. Mistral 7B is a resource-saving open-source model with 7.3 billion parameters that delivers excellent results in word processing tasks despite its compact size. In our article on ChatGPT alternatives we also show many other available providers.
How does Prompting work with MoinAI?
moinAI combines the power of modern GPT models with structured prompting to generate high-quality, context-sensitive responses. The LLM is based on training data from all relevant DACH industries, experience over eight years, and is also trained individually for each moinAI customer. In addition to the option for end users to specifically formulate prompts, internal prompts can also be directed to specifically control the output of the AI. Persona definitions are in use to tailor language, expertise and tonality to the respective target group and task. As a result, answers appear more consistent and application-oriented. Here is a view of the persona control in the hub:
To protect sensitive business information, the model does not store sensitive prompts permanently. There is therefore no risk of data leaks or intellectual property infringement:
To ensure that answers are ethical, neutral and aligned with the company's brand, the model can be defined with predefined guidelines, such as:
- No discriminatory content
- Avoiding sensitive or legally problematic statements
- Use a friendly, professional tone
It looks like this in the moinAI hub:
MoinAI thus offers powerful multimodality with precise, context-sensitive prompting, taking into account persona, task, context and format. There are no data security concerns, as sensitive prompts are not being stored . Protection and communication rules provide reliable and brand-compliant results.
Conclusion
The full potential of large language models can be optimally exploited through effective prompt design. OpenAI provides tried and tested tips with explanations of advanced techniques such as fine tuning. If these are observed, the quality and precision of the expenditure, but above all the accuracy of the results produced and thus user satisfaction, increase significantly. When using SaaS-based generative AI tools, it is particularly important to know the differences between GPT-3.5, GPT-4/o and GPT-5 and to choose a model that is adapted to the use case. Because: Not every model is suitable for every task. moinAI relies on a proven LLM with over eight years of industry experience in the DACH market. Through fine-tuning, AI can learn from dialogs and inquiries individually for each customer in order to generate and summarize content tailored to each customer and to convey knowledge in a targeted manner.
