Today, AI applications are often based on so-called large language models (LLMs). These systems can process, interpret, and generate human language. So it makes sense to use artificial intelligence to write texts. ChatGPT is just one of many solutions that now make this possible. All you have to do is enter a topic and the LLM generates a corresponding result.
However, if you limit yourself to this simple approach, there is a high probability that the result will not meet your expectations. Writing quality, structure, depth of content, topicality, target group appeal, required keywords, source references, links, and other typical requirements cannot be automatically taken into account (or not completely) by these smart assistants. As you may have guessed, the quality of the output is largely dependent on the input. Here, we show you what you should pay attention to in order to create high-quality AI texts.
What is an AI text generator?
The best way to think of an AI text generator is as a digital writing partner that is always available and supports you in a variety of ways with your writing tasks. Such a tool can help you create, revise, or restructure a wide variety of content. Depending on its features, it can even give you tips on comprehensibility or style and check the accuracy of the content. Typical areas of application include emails, social media posts, blog articles, and even longer technical texts.
At its core, an AI text generator works on a dialogue basis: you enter an instruction, known as a prompt, and the application provides you with a (more or less) suitable result. A prompt is a specific task description or briefing for the AI. It specifies (ideally) what should be written, who the text is intended for, and in what form it should appear.
The basic functionality always follows this sequence:
- You formulate your request as precisely as possible.
- The language model analyzes your input and assigns it to known language patterns.
- The AI generates a text that statistically matches your request.
Important: The more precise your specifications are, the closer the output will be to your expectations. Thus, despite all the technological capabilities, your role remains fundamental. You decide which suggestions make sense and where improvements need to be made.
Technologically, these systems are usually based on so-called transformer models, which in turn form the basis of modern LLMs. A well-known example is the GPT architecture developed by OpenAI. These systems are trained with enormous amounts of text and recognize linguistic patterns from it. Even if it sometimes seems that way, they do not “understand” content like humans do, but rather calculate probabilities for word sequences.
AI text generation can be further divided into different processes:
- Prompt-to-text: Automatically generating text with artificial intelligence based on a precise briefing.
- Data-to-text: Conversion of structured data into comprehensible texts, for example for reports or evaluations.
- Image-to-text: Automated description of images or videos in text form – for example, for transcription purposes.
- Text-to-text: Further processing of existing content by summarizing, rephrasing, or making stylistic adjustments.
Some AI text generators are also integrated into other software via programming interfaces. This allows them to become part of optimized processes in editorial, marketing automation, or customer service automation. In some cases, the same technologies can also be used to create images with artificial intelligence. For everyday editorial work, this means one thing above all: greater efficiency, provided that all content is carefully checked.
Tips for using AI text generators
AI can take a lot of work off your hands—but writing good texts with artificial intelligence only works if you actively contribute your own expertise. The decisive factor is always the input. The better your understanding of the text, the better you can control the AI. Truly compelling content is created when humans and AI work together optimally.
There are a few principles you should internalize to help you do this:
- If you want good texts, you can't let AI do the thinking for you.
- For good texts, AI should be understood as a tool for process optimization, not as a replacement.
- Good texts are checked and do not simply accept everything that AI produces.
- Good texts are more than probabilities generated by AI; they are specifically optimized to deliver outstanding added value.
Only with this attitude can you create texts that convince readers (and Google) and actually have the desired effect.
1. Don't settle for mediocrity
The biggest problem with using AI is rarely mastering the technology, but rather a lack of quality awareness. Anyone who creates texts with the expectation that they just have to be there and somehow fit will automatically produce average content. This applies regardless of whether a human or AI is writing. With artificial intelligence, however, the risk of mediocrity is even greater because, unless you specify otherwise, it always assumes the “most probable” option.
The result is unclear statements, lots of repetition, gaps in content, and a lack of depth. In this sense, artificial intelligence acts like a mirror. It shows very clearly how good or bad the source material is. Quality must therefore always be prioritized. You should not assume that the system understands what you and ultimately your target audience need.
A good AI text always requires certain corrections. However, if you start with a clear idea, specify a clean structure, and set concrete goals, the amount of rework will be limited. Without these basics, AI will reinforce existing weaknesses instead of fixing them.
2. Distinguishing good texts from less good ones
Surprisingly often, it is not the craft of writing that determines success, but the ability to evaluate. Those who can recognize whether a text works or not can make targeted improvements. This skill is essential when it comes to AI-generated content.
AI often generates linguistically clean texts that nevertheless have little substance. Statements sound correct, but remain vague or interchangeable. Those who fail to recognize this will accept such content without checking it. Those who read critically will quickly discover a lack of depth on various levels.
In the long term, the most important skill will not be writing with AI, but analyzing and improving these texts.
The following review process has proven particularly effective:
- Writing: You create a first draft, with or without AI.
- Analyzing: Then check for comprehensibility, target audience appeal, and structure.
- Optimizing: Shorten superfluous content, add missing content, sharpen statements.
- Validate: Get feedback from test readers or targeted AI queries.
Additional editing is always a good idea. Some AI text generators can be used specifically for corresponding correction work on spelling, style, or content relevance. However, you remain responsible for the final result.
3. Collect relevant and up-to-date information
Only you really know your target audience. AI models work with probabilities and draw on existing knowledge to interpret what readers expect in a given text context. They can provide helpful suggestions, but they are not always accurate. Caution is advised, especially with current or complex topics.
That's why it's a good idea to incorporate relevant text passages or verified sources directly into prompts. Of course, this takes time, but it also significantly reduces uncertainty and, last but not least, the effort required for later corrections. Your target audience and Google will thank you for it.
4. Be precise with your prompts
A good prompt clearly describes what you expect. Unclear or overly general specifications almost always result in weak texts. The more precisely you formulate, the better the output will be. At the same time, a prompt should be as detailed as necessary, but as concise as possible. Unnecessary digressions also dilute the result.
An example of a clear prompt might look like this:
- Define role: “Write like an experienced editor in the field of urban mobility.”
- Define text type: “Create an informative blog article.”
- Name topic: “Impact of cargo bikes on urban traffic.”
- Determine target audience: “Working adults in large cities.”
- Specify style: “Factual, understandable, without technical jargon.”
- Define scope: “Four paragraphs with a maximum of four sentences each.”
You can make adjustments at any time if the first draft is not yet convincing.
5. Learn and keep improving with AI text generation
The first AI texts can already be very useful, but they usually don't reach their full potential. That's normal. Text quality never happens immediately, but develops step by step. The more you work with AI, the better you will understand its strengths and limitations.
It is important that you do not view your texts as a one-time result. Good content benefits from revision, comparison, and fine-tuning—we have seen this many times before, but it is also simply that important. With each iteration and each new project, your sense of which instructions work and which do not will grow.
To help you improve more quickly, it's worth establishing a clear workflow:
- Work iteratively: Allow each text to mature. A good draft benefits from revision. Apply what you've learned from past projects to new ones.
- Compare variants: Have several versions created and check their impact and clarity.
- Change perspectives: Request alternative viewpoints or styles.
- Document successful prompts: Saving proven instructions saves time and ensures quality in the long term.
Simple editorial rules also help:
- One idea per paragraph increases comprehensibility.
- Clear reader guidance through highlighting and structure facilitates absorption.
- A precise target group definition prevents wastage.
In practice, hybrid working methods, in which people specify the direction, structure, and evaluation, have proven successful. AI provides formulations, variants, and alternative approaches. Only the right interaction brings real benefits.
And which tool can be used to create AI texts?
If you follow the principles mentioned above, you can achieve good results with many modern applications. The choice of the right tool depends primarily on what types of text you want to create. Some solutions are suitable as all-rounders, while others are specialized for specific tasks. You can often use artificial intelligence free of charge before committing to it in the long term.
There have been many recent developments in modern language models. Current versions such as GPT-5.2 rely more heavily on context understanding, longer text coherence, and more precise style control. For text creation, this means more stable results for longer content, fewer repetitions, and better implementation of detailed prompts. However, the responsibility for checking and classifying the text remains with the user. ChatGPT from OpenAI is therefore particularly suitable for versatile text tasks. It can generate content in different languages and styles and respond very flexibly to specifications. This makes it suitable for both short texts and extensive articles. Content corrections, SEO tips, and much more are possible
Jasper AI is aimed more at marketing and SEO users.
The tool provides templates for different types of text and offers broad support for search engine optimization. Additional features such as structural aids and plagiarism checks make everyday editorial work easier. Use is always subject to a fee.
Neuroflash is particularly geared towards German-language content. The solution offers functions for text adaptation, rewriting, and SEO support. An important aspect is its focus on data protection requirements within Europe. With the free version, up to 2,000 words can be generated per month.
Writesonic is also widely used. Similar to Jasper, this tool focuses primarily on marketing and advertising texts, but can also generate any other form of text. Over 100 templates make it easy to use. Summarization, paraphrasing, and abridgment functions make it easy to quickly adapt text. You can also experiment with different models such as GPT, Claude, or Gemini. Getting started is free with up to 10,000 words per month.
Conclusion
Good texts with artificial intelligence do not come without effort. Technology can help, but it cannot replace a genuine understanding of the text. Target group analysis, thematic classification, and clear objectives remain your tasks. Those who engage with the tools and understand how they work will continuously improve the quality of their output.
Real added value is created when you use AI consciously and remain critical. Learn to formulate precise instructions, check results, and make targeted improvements. Over time, AI text generators become helpful tools that can massively increase your editorial efficiency and open up new possibilities.
FAQ
Which AI is best for text creation?
That depends on what kind of text you want to create. All-rounders such as ChatGPT are suitable for versatile tasks, while specialized tools such as Neuroflash can be useful for marketing or SEO. Good results can be achieved with all modern solutions if they are used correctly.
How do you write a text with AI?
First, define your goal, topic, and target audience. Then formulate a clear prompt with style and scope specifications. Enter it into the prompt mask of the respective tool and start the generation process. Finally, check and revise the text until the content and tone are right.
How does an AI text generator work?
An AI text generator is based on large language models. These analyze texts from their training and calculate suitable word sequences. Based on your input, the model generates a new text that statistically matches the query.
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Jens ist Mitgründer von bakedwith, einer Boutique-Beratung für smarte Automatisierung und KI. Er unterstützt mittelständische Unternehmen und Konzerne dabei, Prozesse zu optimieren, manuelle Arbeit zu reduzieren und durch effiziente Workflows Wachstum zu erzielen. Zuvor war Jens Growth Lead bei OMR und begleitete als Freelancer zahlreiche Unternehmen bei der Optimierung ihrer CRM- und Automationssysteme. Seine Leidenschaft gilt der Verbindung von Wachstum und Effizienz – damit Teams sich auf das Wesentliche konzentrieren können.
