Prompt engineering is quickly becoming one of the most desirable skills for an IT professional. Although 81% of these professionals are self-confident that they can use AI successfully, there are actually only 12% that truly have the skills required to perform it effectively. Without an understanding of how to get the most value from their AI resources, there will be a huge part of this cutting-edge technology’s capabilities that go unleveraged.
AI is still a relatively new force in the professional world, so skill gaps are common. In fact, 60% of IT decision makers claim that AI constitutes their largest skill gap across their workforce. 70% of global workers are in agreement that their skills need to be drastically upgraded to meet the demands of their employers. These demands are spiking sharply - two out of every three leaders claim they wouldn’t hire someone that isn’t familiar with AI. It is clear that learning AI skills is clearly attractive for future employment opportunities.
There is currently a 50% hiring gap between AI jobs and AI job roles, meaning someone can be a very competitive candidate if they skill up on their AI literacy. This often means improving prompt engineering skills, which are described as writing the instructions that a generative AI platform can then interpret and follow. Some of the ways to structure a prompt include chain-of-thought, generated knowledge prompting, least to most prompting, and non-text prompting. Most professionals may be hard pressed to even describe the difference between each of these styles.
