Within the past 5 years, AI technology has exploded in popularity. It’s gotten to the point where you have almost certainly heard of ChatGPT, and likely alternative AI like Gemini or Microsoft’s Copilot. While this technology has become incredibly intuitive, there is still a correct and incorrect way to prompt AI for the best results.
Just like a normal conversation, being clear is vital. If I cannot properly convey my request to an AI, it is unreasonable to assume I would get a response that would accurately answer my request. In a similar vein, prompts should attempt to be concise. The longer a response is, the more likely it is to be unclear or misinterpreted. And, oftentimes, a prompt may need to be regenerated or reiterated several times in order to construct the best response from this AI. Although simple skills, it is a deficiency in this area leads to workers not being well-versed enough in AI prompting. In fact, 81% of IT professionals believe they can correctly utilize AI, but it is estimated that only 12% actually have the skills to engineer prompts.
As many as 3 in every 5 IT decision makers say AI is their biggest skill gap, and around 70% of global workers need to upgrade their AI skills in order to be brought up to current speed. Skills such as how to get the most value from generative AI and how to effectively use it at work are simply missing from most desk workers, who only know the basics of AI. It is also imperative that AI prompt engineers leverage trusted data sources in order to keep their first-party data secure. These factors contribute to a 50% hiring gap between AI jobs and AI roles, meaning that becoming an AI expert is more important than ever.
So, how can we bridge the gap between how we think AI works and how it actually works? AI prompt engineering is defined as the art and science of structuring an instruction that a generative AI can interpret and follow. However, this definition allows for a variety of prompt types to be utilized for AI. This could be something like a chain-of-thought or chain-of-symbol prompts, which teach the AI to answer smaller, intermediate steps in order to prove their response to the user’s prompt. Similarly, generated knowledge prompting is prompting that involves building on previous user responses to better answer that user’s question. Finally, prompt developers can also use least-to-most prompting as a way of providing the minimal amount of assistance to allow AI to learn the most efficiently.
But you don’t even need to use text to prompt AI. When utilizing images and video to prompt AI, you can use negative prompts, which work by telling AI exactly what you don’t want it to say or generate in a photo or video. Conversely, you can use textual inversion, which works by providing AI with a set of training images and asking it to generate similar images to the set. Because of all these techniques, it can be very difficult to be an effective prompt engineer without training to keep you refreshed and up to date.
This disconnect is becoming a growing problem, as 72% of IT leaders believe AI skill gaps are an urgent and pressing issue as of today. When it comes to training and retraining AI experts, YU Global is an emerging leader for this path. They currently provide reskilling and cross-skilling across a variety of courses. For first-time AI learners, they offer an intro to AI, several advanced prompt engineering courses, and AI strategies for business and organizational leaders. For those interested in specified learning, they offer AI for Financial Management, AI for Marketing, and AI for Excel. They will also soon offer AI for education, entrepreneurship, human resources, and project management.
Whether you are new to prompt engineering or want to upgrade your resume, learning AI is becoming easier and easier. With a variety of courses to choose from, you can learn or relearn your AI prompt engineering in as little as 6 to 8 weeks. Regardless of your current position, YU Global offers the best way to bring yourself up to speed with AI.
