The Future of Prompt Engineering

The Future of Prompt Engineering

Throughout 2023, the theme of artificial intelligence has taken over discussions across industries and disciplines. With brands and companies around the world clamoring to incorporate AI software into their respective toolboxes, it does not seem that the AI wave will be dying down anytime soon. With the rise of AI, there comes a related question: what will work look like in an AI-assisted world, and how will workers have to adapt?

While the future of artificial intelligence in the professional world is still very much uncertain, investment into putting AI to work for brands and companies has recently taken the form of figuring out how different AI software works, and how to get desirable results. Since most AI software available today returns text, images, and other information in response to written prompts, this practice has come to be known as “prompt engineering.”

What is Prompt Engineering?

Behind all the AI software we’ve played around with like ChatGPT and Midjourney is a powerful science called natural language processing, or NLP. Traditionally, “speaking” to computers means learning different programming languages, which lets users input precise and specific prompts in return for the machine’s computational power and functionality. While anybody can learn how to code, programming languages are certainly not as immediately intuitive as our regular speaking languages, and can serve as a barrier to entry for anybody who wants to “communicate” directly with computers for whatever purpose.

Natural language processing is an attempt to lower this barrier of entry by providing a means through which to communicate with computers directly, through our natural languages. When you talk to ChatGPT, you use regular language, not code or anything technical per se. However, using different words and different phrases, and feeding ChatGPT information in specific ways, might get you better results, or results more aligned with what you’re looking for. This is prompt engineering. Figuring out how and what to ask in order to get back information that is useful to you.

AI as an Evolving Tool

Much of the fear and uneasiness around the rise of AI has been around the idea that artificial intelligence will replace humans as workers across several industries. In workplaces plagued with blind profit-seeking and little regard for the wellbeing of their workers, this is certainly a valid fear to hold. That said, there is another important perspective to consider: AI is a tool.

A similar conversation was had when digital artwork hit the animation world. Artists resisted and feared that computers would take their work, and while traditional animation certainly became a less common practice, the change was not so much that animators were out of a job, but that they learned to pick up a stylus instead of a pen. The future of AI in the professional world is ambiguous to be sure, but AI prompt engineering may very well be to content creation what digital artwork was to animation. It is not necessarily that copywriters and graphic designers will be out of a job, but perhaps that they will have to take AI generation on as an additional skill in their tool belt.

The Future of Prompt Engineering

In the current state of the art, prompt engineering is an important and well-compensated role for two reasons. First, this is very new territory, so few people are very familiar with being successful in this skill. And second, natural language processing still has a way to go. As things stand, ChatGPT and other programs are pretty good at figuring out what you mean by your prompts, but it often definitely feels like there’s something getting lost in translation. This makes figuring out exactly how to communicate with the software efficiently an important skill, for now.

As NLP gets better, it will be much easier and much quicker to get good and great responses from AI software with less technical prompts. Eventually, we may be able to have seamless and complex conversations with our AI counterparts, like Tony Stark does with Jarvis in the Iron Man movies. Exactly how quickly this will happen, and if it will happen at all, however, are questions that we simply do not have answers to. Different tools also have different NLP approaches as well, meaning that seamless conversation with one AI tool might not imply the same accessibility across tools.

On one extreme of the spectrum, advances in NLP may mean that prompt engineering fades as a technical niche altogether, since most prompts will be perfectly understood no matter the technical context. On the other extreme, prompt engineering will always be important if NLP never reaches the current sci-fi levels we imagine that it will. The reality, likely, will be somewhere in between, where NLP will be precise enough to handle most prompts efficiently in most contexts, but not always. For the immediate future, however, it seems that prompt engineering is a necessary investment for brands that want to implement these AI tools as quickly as possible.