GPT-4 and the Flipped Interaction Pattern

Like me, you’ve probably been dazzled by the strides recently made in the field of Generative AI. It feels like we are living in a science-fiction reality, where machines understand us.

At the heart of this revolution, OpenAI’s GPT-4 has emerged as a marvel in the realm of language models. As someone who had the privilege of playing around with it, I can testify to its astonishing capabilities.

In this article I want to share a technique that intrigued me quite a lot. In literature it is referenced as the Flipped Interaction Pattern, and I found it to be a very powerful framework to help in problem solving tasks. The distinct aspect of this prompt engineering method lies in its “reversed” (i.e. flipped) approach: instead of directly asking for answers or solutions from the AI, it primarily focuses making the AI deliver the right questions that could effectively drive us towards our desired solution.

In this article, I’ll walk you through how it works, why it’s beneficial, and provide real-life examples of its effectiveness. By the end of it, I hope you’ll be equipped with a useful technique – one that can be applied to a vast array of problems, possibly with results that may surprise you.

In today’s world, the depth of our knowledge can often be compared to a pool — long and narrow. We dive deep into some specialties, becoming experts in some specific domains. This hyper-specialization allows us to excel in our respective fields, but at the same time, it can leave us feeling lost when we encounter problems outside our areas of expertise. As a software engineer, I may be well-versed in algorithms, data structures, and coding, but put a spade in my hand and ask me to tend a garden, and I will transform any green into a wasteland.

This is where the Dunning-Kruger effect may come into play. As per this psychological hypothesis, there’s a cognitive bias where people with low ability at a task

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