Is Prompt Engineering Vanishing or Is It the Future?

Our relationship with technology is always changing. The field of artificial intelligence (AI), in which robots are taught to think, learn, and even speak like people, is one of the most fascinating contemporary developments. In the midst of all the advancements in fields like generative AI, prompt engineering is a delicate skill that is becoming more and more popular. Consider engaging in a dialogue with a machine in which you give it a cue or a "prompt," and it reacts by providing pertinent data or actions. That's what prompt engineering is all about. It involves formulating the ideal queries or directives to direct AI models—particularly Large Language Models (LLMs)—to generate the intended results.

Prompt engineering has taken the generative AI world by storm. The job has been hailed by World Economic Forum as the number one “job of the future” while Open AI CEO Sam Altman characterized it as an “amazingly high-leveraged skill.” Social media brims with a new wave of influencers showcasing “magic prompts” and pledging amazing outcomes.

But even with all of the hype, prompt engineering might not be as popular as it once seemed for a number of reasons. First, as AI systems advance, the requirement for carefully designed cues will decrease as they become more perceptive and proficient at comprehending natural language. Second, the field of prompt engineering is about to become obsolete due to the advent of new AI language models like GPT4, which already demonstrate significant potential in this regard. Finally, the usefulness of prompts varies throughout different AI models and versions because their effectiveness depends on the particular algorithm.

So what is a more durable and flexible ability that will keep allowing us to take advantage of generative AI? It is the capacity to recognize, evaluate, and define problems. It is also known as problem formulation.

Problem formulation and prompt engineering differ in their focus, core tasks, and underlying abilities. Prompt engineering focuses on crafting the optimal textual input by selecting the appropriate words, phrases, sentence structures, and punctuation. In contrast, problem formulation emphasizes defining the problem by delineating its focus, scope, and boundaries. Prompt engineering requires a firm grasp of a specific AI tool and linguistic proficiency while problem formulation necessitates a comprehensive understanding of the problem domain and ability to distill real-world issues. The fact is, without a well-formulated problem, even the most sophisticated prompts will fall short. However, once a problem is clearly defined, the linguistics nuances of a prompt become tangential to the solution.

Unfortunately, problem formulation is a widely overlooked and underdeveloped skill for most of us. One reason is the disproportionate emphasis given to problem-solving at the expense of formulation. This imbalance is perhaps best illustrated by the prevalent yet misguided management adage, “don’t bring me problems, bring me solutions.” It is therefore not surprising to see a recent survey revealing that 85% of C-suite executives consider their organizations bad at diagnosing problems.

How can you get better at problem formulation? By synthesizing insights from past research on problem formulation and job design, we identified four key components for effective problem formulation: problem diagnosis, decomposition, reframing, and constraint design.

v  Problem Diagnosis: Problem diagnosis is about identifying the core problem for AI to solve. In other words, it concerns identifying the main objective you want generative AI to accomplish. Some problems are relatively simple to pinpoint such as when the objective is gaining information on a specific topic like various HRM strategies for employee compensation. Others are more challenging such as then exploring solutions to an innovation problem.

v  Problem Decomposition: Problem decomposition entails breaking down complex problems into smaller, manageable sub-problems. This is particularly important when you are tackling multifaceted problems, which are often too convoluted to generate useful solutions.

v  Problem Reframing: Problem reframing involves changing the perspective from which a problem is viewed enabling alternative interpretations. By reframing a problem in various ways, you can guide AI to broaden the scope of potential solutions, which can, in turn, help you find optimal solutions and overcome creative roadblocks.

v  Problem Constraint Design: Problem constraint design focuses on delineating the boundaries of a problem by defining input, process, and output restrictions of the solution search. You can use constraints to direct AI in generating solutions valuable for the task at hand. When the task is primarily productivity-oriented, employing specific and strict constraints to outline the context, boundaries, and outcome criteria is often more appropriate. In contrast, for creativity-oriented tasks, experimenting with imposing, modifying, and removing constraints allows exploring a wider solution space and discovering novel perspectives.

Overall, ensuring that AI results are in line with task objectives and promoting productive cooperation with AI systems requires developing expertise in problem diagnosis, deconstruction, reframing, and constraint design. While prompt engineering might be the buzzword of the day, its long-term importance is limited by its lack of sustainability, adaptability, and transferability. It may even be detrimental to place too much emphasis on creating the ideal word combination, as this might take attention away from the investigation of the issue at hand and reduce the creative process' sense of agency. Instead, the secret to navigating the unpredictable future with advanced AI systems may lie in developing your issue formulation skills. It could end up being just as important as learning programming languages in the early days of computers.

****************************
********************
************