So we're concerned with the art of prompt engineering, i.e. how the right commands for a Generative Pre-Trained Transformer (GPT) Model can be designed and consistently used. What exactly that is, prompt engineering, which aspects need to be considered and how this technology could be used in the future, will be discussed shortly in this article.
What is prompt engineering?
Prompt engineering is commonly understood as the iterative sharpening of commands that provide the desired output as part of generative AI. This is a process which, using various techniques, is intended to approximate such a command structure that is able to provide consistent answers that require as little subsequent correction as possible. Since every company needs its very own way of addressing and tonality of communication measures, it is incredibly important to fine-tune the use of generative AI tools so that the result is an initial text that can be used over long distances. So that true efficiency can take hold, it is necessary to work on a unique prompt structure – one of the leverages in the world of current marketing and corporate communication.
Key Aspects of Prompt Engineering
As already mentioned in the section above, generative AI tools do not guarantee efficiency per se: using them adequately requires a certain amount of experience and, of course, in-depth knowledge of one's own company.
The important specifications of the prompts to be created include the following textual aspects:
1. Tonality: What should the writing sound like? (Explanatory, discursive, playful, prosaic, etc.)
2. Role: It should mean who Speaks to whom? Seller to customer? Expert to expert? Teacher to student?
3. Explicit address: How should the audience be addressed? This includes questions such as: Is there now an explicit shower, or do you sigh yourself, in accordance with a more conservative, perhaps more serious logic?
4. Context: In which area do you move semantically? Do certain terms possibly exist (Buzz Words) that should fall?
5. Knowledge of the designated target group: What is the score of their search habits in order to fix relevant keywords.
In addition, especially in view of the constantly progressive development of generative AI, it is absolutely necessary to approach the tools and art of prompt engineering experimentally and largely playfully. The black box of Generative Pre-Trained Transformer invites you to embark on the adventurous endeavor following a trial and error logic.
Despite all the euphoria, a certain degree of skepticism about the results is warranted. For example, quality assurance of the factual accuracy of generated text elements by thoroughly informed employees cannot be dispensed with. Especially at the beginning of working with emphatically iterative prompt engineering, it is important to maintain a critical eye. Is the reading flow correct? Where in the generated text is it still bumpy? What could be the reason for that? Does the text have to be corrected manually, or can the situation be remedied sustainably and easily by meticulously sharpening the prompt? The topic of SEO, which is so important for any marketing, should also be considered. How optimally does the generated content work after publication? The downstream tracking of content once produced should also be taken seriously as an essential part of procedural prompt engineering. Since the AI models in circulation can sometimes change very rapidly, it is particularly necessary to regard the use of generative AI as an experiment that does not (yet) know any solutions that come across as completely fixed, or even canonized.
conclusion
Prompt engineering is a pioneering endeavor, but it involves a lot of work. To mean an efficient future, it takes some dedication and patience to fix process routines, identify responsibilities and thus ultimately ensure more business-promoting security.
