Using neural networks to write texts and create other types of content brings not only joy but also some inconveniences. Human thinking differs from how artificial intelligence works. While a real author has a category of knowledge known as “obvious”, an algorithm does not. This difference in perception causes more problems than it seems.
You might wonder why “just pressing a button” doesn’t work, what major mistakes plague your generated texts when working with AI, and how to leave all that behind. But the answer is actually simple: you aren’t paying enough attention to prompts, the difference in perception, and how algorithms work. We’ll try to address these issues in this article.
Prompt: What It Is and How to Write the Perfect Task for AI
A prompt is a standard text-based technical specification. For a copywriter preparing an article, it’s necessary to compile a list of parameters and keywords. Only by meeting this condition can you obtain high-quality text. However, for AI, the task preparation process is slightly different, though the quality of the output remains the same.
So, what is the anatomy of the perfect prompt? There are 5 mandatory elements of a technical assignment for neural networks. These include:
- Topic. The more precisely you define the goals for using the content, the better the quality will be. The algorithm cannot read minds, so it’s worth compiling a list of preferred topics for the future article in advance.
- Audience. The writing style will depend on the article’s target audience. You should take into account the audience’s expectations, the readers’ age, and other factors. The overall tone of the text and its effectiveness will depend on this.
- Style. A business, conversational, or expert tone affects readers differently. You can ask the AI for advice, and it will suggest alternative options. Texts for neural networks and how to write a prompt so you don’t have to rewrite the AI’s output are interrelated concepts. At the same time, it’s important to consider the previous characteristics and suitable topics.
- Structure. Writing content according to a set plan is always better than vague requirements. Write a preliminary outline of the article and submit it to the algorithm. It will generate the text not only based on the outline but also on other required parameters.
- Length. To write the text, you need to specify the length. Usually, the number of characters including spaces is indicated. The reason is that with other requirements, line breaks can be removed entirely, turning the text into a string of letters. Also keep in mind that not all neural models are trained to count the number of characters.
Style and tone of voice (ToV) set the right tone for communication. This means you must decide on the writing style in advance and only then move on to the next step. Sometimes you need to ask the algorithm itself for advice. It helps users improve their work and prepare the necessary high-quality text.
Common Mistakes
We always recommend following the outlined plan. First, work on the prompt, and only then move on to the next step. Think about the audience that will be reading the text you create. If you’re disappointed with the result, check the technical specifications for the following errors:
- Vague requirements. The more precise the task, the better the result. Remember that the algorithm needs to understand who is in the target audience. Context plays a key role in the final text, so try to figure out how to explain to the machine who your client is.
- Incomplete prompt. By specifying the role for the neural networks, you can have the text written from the perspective of the company’s marketer or owner. To do this, clearly indicate the writing style. You can request that the article be written in an expert and competent manner. The algorithm doesn’t refuse to work; it simply interprets the task in its own way.
- Fact-checking and AI “hallucinations.” How to avoid publishing falsehoods? Verify the provided information yourself or through additional sources. The system often “invents” non-existent facts. If a reader notices them, hoping for a good result is, at the very least, foolish.
- Clichés. Avoiding clichés in texts is very important. They make it difficult to process information and cause fatigue or boredom. To get the expected result, try to specify your requirements in advance. Indicate that bureaucratic jargon should be avoided if the article is not intended for experts, and vice versa.
- Overall result. Only precise wording can prevent generalizations in texts. There are no simple truths for an algorithm, since in its digital world, the system is based on real parameters and requirements. What will a neural network never do for the author? It will never account for the human factor. Errors arise because the user did not specify a parameter in their technical specifications.
The algorithm’s suggestions should be viewed as a guide to action. The outcome of the work depends on how responsibly you approach the task.
Editing after the neural network: a checklist for refining the material
Robots cannot empathize, and therefore emotional intelligence is absent from the texts. You can change this by following a specific set of steps during the review phase. To do this:
- First, assess the meaning. Read the entire text and make sure it truly addresses the task at hand. Sometimes the neural network strays off-topic or adds unnecessary paragraphs.
- Check the facts. Algorithms can make mistakes with numbers, dates, or names. If the text is expert or informational, it’s important to double-check the data against reliable sources.
- Pay attention to the structure. A piece of writing with a clear introduction, main body, and logical conclusion is the result of good work. Rearrange paragraphs or add subheadings.
- Remove clichéd phrases. Neural networks often use phrases like “in today’s world” or “it is important to note.” Such phrases make the text less lively.
- Check the style. Make sure the tone of the content is appropriate for the platform and audience. Sometimes it’s enough to simplify the wording and shorten overly long sentences.
Neural networks significantly speed up the creation of text, but almost every result requires a little refinement
