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-5 Model Series
The GPT-5 family refers to OpenAI’s current generation of models. It consists of a system comprising several coordinated model variants. At its heart is an intelligent routing system that automatically forwards queries to the most appropriate component within the family.
The family comprises three functional areas: a powerful main model for general tasks, a ‘Thinking’ variant for complex reasoning, and smaller, efficiently optimised models for fast and resource-efficient responses. An integrated router dynamically determines which variant to use based on complexity and the expected depth of the response.
At the application level, this means a greater decoupling of user interaction from the specific model selection. As a result, prompting is not geared towards a single model, but rather towards the system behaviour of the model family as a whole. An overview of all OpenAI language models can be found on the website.
Development of OpenAI models
The development of GPT models has progressed rapidly in recent years: GPT-3.5 made generative AI accessible to a wide audience for the first time through ChatGPT and established itself as a powerful model for text generation and chatbots. GPT-4 brought significant improvements in accuracy and contextual understanding. The breakthrough in this model was: multimodality – that is, the ability to process not only text but also images and subsequently videos – was implemented and is now standard in large language models. With the popular GPT-4o (“Omni”), OpenAI finally introduced a faster, more cost-effective and fully multimodal model that combined text, images, audio and other input formats within a single model. These developments form the basis of today’s GPT-5 model family, which further expands on the capabilities of its predecessors and provides different model variants for speed, cost and reasoning, depending on the use case.
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
Prompt design remains a key factor in realising the potential of large language models (LLMs). Best practices from OpenAI show that the quality and consistency of results can be significantly improved, above all, through clear instructions and robust prompting approaches. With model families such as GPT-5, the focus is shifting towards the appropriate area of application within a system; the key factor here is selecting the right setup depending on the use case in SaaS-based AI applications.
Despite these advances, it remains the case that results are not always error-free and should be checked. However, correctly applied prompting remains the key lever for achieving better results. In chatbot solutions such as moinAI, prompts control behaviour and response logic, thereby enabling the targeted use of generative AI for information provision and content creation.
