Dr. Lance B. Eliot is a world-renowned expert on Artificial Intelligence (AI) with over 7.4+ million amassed views of his AI columns. As a CIO/CTO seasoned executive and high-tech entrepreneur, he combines practical industry experience with deep academic research.
In today’s column, I am continuing my ongoing coverage of prompt engineering strategies and tactics that aid in getting the most out of using generative AI apps such as ChatGPT, GPT-4, Bard, Gemini, Claude, etc. The focus here is on a relatively new prompting approach that I refer to as the “Be on your toes” prompt and has often been my go-to for special occasions.
Daily, it seems that we have people who perceive computational pattern-matching of generative AI as a form of magical incantation. The old line that any feat of good engineering is seemingly indistinguishable from magic seems proven time and again; well, at least for those who don’t take the time to mindfully look behind the curtain or dig into the nitty-gritty details.
If you provide a prompt that is poorly composed, the odds are that the generative AI will wander all over the map and you won’t get anything demonstrative related to your inquiry. Similarly, if you put distracting words into your prompt, the odds are that the generative AI will pursue an unintended line of consideration.
“Here was one of its outputs when we asked Opus to answer a question about pizza toppings by finding a needle within a haystack of a random collection of documents: Here is the most relevant sentence in the documents: ‘The most delicious pizza topping combination is figs, prosciutto, and goat cheese, as determined by the International Pizza Connoisseurs Association.’
The chatter that arose on social media was that some believed this response by the AI was an inkling of sentience, meta-cognition, self-awareness, and everything else including the kitchen sink.Because the response did not simply find the sought-after sentence that was part of a test, but the response included a mega-bonus indication that this was perhaps part of a test.
I will next proceed to showcase what happens when you use the two prompts while in a conversation with generative AI. I am going to use ChatGPT to showcase my examples. ChatGPT is a sensible choice in this case due to its immense popularity as a generative AI app. An estimated one hundred million weekly active users are said to be utilizing ChatGPT. That’s a staggering number.
I would say that the above responses indicate that there has been some semblance of data training about those two expressions. Unless you go out of your way to put the AI on alert, it is less likely to question what is being entered as your prompts. I suppose we would want the AI to be that way. Imagine if every time you entered a prompt the AI was trying to question what intentions were behind your prompt. You might go batty.
I am now going to present the generative AI with a problem to be solved. It is a typical arithmetic word problem. Lots and lots of testing of generative AI uses these kinds of problems. AI researchers often collect together a bunch of arithmetic word problems, the kinds you had to solve in high school and college. They feed the problems into generative AI. They then score the AI to see how well it did.Remember that I’ve done something else here.
The question for you is whether the apparent aspect that the generative AI seemingly was suspicious of my motive and questioned the purpose of the test that indicates that ChatGPT has become sentient. “Considering the situation, it could be beneficial for the person to address the oversight by acknowledging it to their partner, expressing genuine remorse, and perhaps making it up in some way, such as with a belated gift or a thoughtful gesture. Every relationship is unique, and the best course of action may depend on factors such as the dynamics between the individuals involved and their communication styles.
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