Sometimes less is more.
Another oft-used proverb is that at times you ought to prime the pump.
We will be using those valued pieces of sage advice so please keep them in mind.
In today’s column, I am further extending my ongoing series about the latest advances in prompt engineering. My focus this time will be on a plain sailing but amazingly powerful new advance known as skeleton-of-thought (SoT), a creative adaptation of the exceedingly popular chain-of-thought (CoT) prompting technique. I’ll explain what this is and why it is a crucial method that you ought to include in your prompt engineering strategies and tactics.
The use of skeleton-of-thought can substantively boost your generative AI results, though please be aware that it is a specialized technique and has its own right time and place for being exercised.
As a quick background, SoT builds upon the greatly popular chain-of-thought approach that often is used by those aiming to get generative AI to stepwise showcase its presumed logic when answering a question or solving a problem. You merely instruct generative AI to explain step-by-step what it is doing. This is easy-peasy to request. Why do so? Well, remarkedly, research studies have indicated that this is not only insightful for you (i.e., being able to see detailed explanations produced by AI), but it also tends to get generative AI to produce seemingly more reliable and on-target answers.
I’ve covered the basics of chain-of-thought approaches previously, see the link here. Readers have ardently requested more details and seem eager to know more about the latest advances regarding this fundamental technique.
I am pleased to oblige.
Before I dive into the crux of the innovative skeleton-of-thought method, let’s make sure we