Ideation and problem-solving

When designers respond to a problem, the problem is typically on a sliding scale from simple to complex.  Roughly speaking, simple problems can be described as puzzles, or "tame problems", whereas complex problems are known as "wicked problems". A puzzle is fairly straightforward when the pieces need to be located and connected. That is, it is assumed that the solution to the puzzle is almost certain to be found. A complex problem, in contrast, has no known or at least immediate solution and so designers are faced with challenges that call for a greater variety of skills and capabilities involving collaborative and decision-making tools and techniques. But whether simple or complex, the starting point for problem solving includes clarifying the goal, identifying the constraints, and understanding the context of the situation. Also, see Tame vs Wicked problems, in blog below. https://www.td.org/content/atd-blog/puzzle-problem-challenge-or-conundrum

Conversational prompting

Prompt engineering is the process of writing effective instructions for a GPT model, such that it generates content that meets desired outcomes. And because the content generated from a model is non-deterministic, that is, even for the same input, the model can generate different responses on different runs - and the rich datasets combine art and science, prompting can be particularly useful in creative fields such as design. However, there’s more to effective human-AI collaboration than a perfect prompt. And so, conversational prompting is a technique that involves interacting with AI systems like ChatGPT in a human-like conversation. That is, users describe in everyday terms what they want ChatGPT to do rather than trying to craft complex prompts. In this, users engage in back-and-forth interactions with the model to refine the results, provide additional context, and answer the ChatGPT's questions. That is, the user guides the process while letting ChatGPT handle the specifics of generating appropriate prompts and responses. Problem solving, then, it is thought, is enhanced by balancing human ingenuity and machine intelligence. However, conversational prompting, as a feed-back model, carries risks too. That is, the interaction may contain misinformation, biases or illusions raising the question: Is the output reliable and trustworthy? Selected sources:  https://platform.openai.com/docs/guides/prompt-engineering    https://promptengineering.org/conversational-prompting-in-generative-ai/