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AI Prompt Engineering Isn’t the Future

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Summary

Prompt engineering has become a popular skill in the generative AI world, however, its prominence may be fleeting due to the fact that AI is getting better at understanding natural language and can craft its own prompts. To be successful in harnessing the potential of generative AI, problem formulation skills are necessary. Problem formulation is the ability to identify, analyze, and delineate problems which is different from prompt engineering which focuses on crafting the optimal textual input. Problem formulation requires a comprehensive understanding of the problem domain and the ability to distill real-world issues. Problem formulation is made up of four components: problem diagnosis, decomposition, reframing, and constraint design. In order to effectively reframe problems, it is important to take perspective of users, explore analogies, use abstraction, and question problem objectives or identify any missing components. Problem constraint design involves delineating the boundaries of a problem by defining input, process, and output restrictions of the solution search. By mastering problem formulation, it is possible to navigate the uncertain future alongside sophisticated AI systems.

Q&As

What is prompt engineering?
Prompt engineering is the job of optimizing textual input to effectively communicate with large language models.

What is problem formulation and why is it a more enduring and adaptable skill than prompt engineering?
Problem formulation is the ability to identify, analyze, and delineate problems. It is more enduring and adaptable than prompt engineering because it is not reliant on a specific AI tool and it requires a comprehensive understanding of the problem domain.

What are the four key components for effective problem formulation?
The four key components for effective problem formulation are problem diagnosis, decomposition, reframing, and constraint design.

How does problem reframing help generate alternative solutions?
Problem reframing helps generate alternative solutions by changing the perspective from which a problem is viewed, enabling alternative interpretations.

What are the advantages of problem constraint design?
The advantages of problem constraint design are that it delineates the boundaries of a problem by defining input, process, and output restrictions of the solution search. It can also direct AI in generating solutions valuable for the task at hand.

AI Comments

👍 This article shows how problem formulation is an important and highly relevant skill to have when dealing with AI and how it helps to generate effective solutions.

👎 This article fails to provide concrete examples of how problem formulation can be implemented in practice.

AI Discussion

Me: It's about how Prompt Engineering is not the future of AI. It argues that problem formulation is the more enduring and adaptable skills for harnessing the potential of generative AI.

Friend: Interesting. What are the implications of this article?

Me: Well, it suggests that problem formulation is the more important skill for both AI engineers and executives. It also implies that problem reframing and constraint design are essential for aligning AI outcomes with task objectives and fostering effective collaboration with AI systems. Lastly, it emphasizes that overemphasizing the crafting of the perfect combination of words can be counterproductive, as it may take away from exploring the problem itself.

Action items

Research problem formulation techniques such as the “Five Whys” technique, functional decomposition, work breakdown structure, perspective taking, analogies, abstraction, and proactively questioning problem objectives.
Practice problem formulation by breaking down complex problems into smaller, manageable sub-problems and reframing the problem from different perspectives.
Experiment with imposing, modifying, and removing constraints to explore a wider solution space and discover novel perspectives.

Technical terms

AI: Artificial Intelligence - a branch of computer science dealing with the simulation of intelligent behavior in computers.
Prompt Engineering: The job of optimizing textual input to effectively communicate with large language models.
Generative AI: Artificial intelligence that is able to generate new content based on existing data.
Problem Formulation: The ability to identify, analyze, and delineate problems.
Five Whys: A problem-solving technique used to explore the cause-and-effect relationships underlying a particular problem.
Functional Decomposition: A problem-solving technique used to break down complex problems into smaller, manageable sub-problems.
Work Breakdown Structure: A problem-solving technique used to visually depict complex problems and simplify the identification of individual components and their interconnections.
Perspective Taking: The ability to view a problem from different angles.
Analogies: A comparison between two different things that are alike in some way.
Abstraction: The process of reducing a complex problem to its essential elements.
Constraint Design: The process of delineating the boundaries of a problem by defining input, process, and output restrictions of the solution search.