The world of immediate engineering is fascinating on varied ranges and there’s no scarcity of intelligent methods to nudge brokers like ChatGPT into producing particular sorts of responses. Methods like Chain-of-Thought (CoT), Instruction-Based mostly, N-shot, Few-shot, and even methods like Flattery/Position Task are the inspiration behind libraries filled with prompts aiming to satisfy each want.
On this article, I’ll delve into a method that, so far as my analysis reveals, is probably much less explored. Whereas I’ll tentatively label it as “new,” I’ll chorus from calling it “novel.” Given the blistering price of innovation in immediate engineering and the benefit with which new strategies may be developed, it’s solely attainable that this method would possibly exist already in some kind.
The essence of the method goals to make ChatGPT function in a means that simulates a program. A program, as we all know, includes a sequence of directions sometimes bundled into features to carry out particular duties. In some methods, this method is an amalgam of Instruction-Based mostly and Position-Based mostly prompting strategies. However in contrast to these approaches, it seeks to make the most of a repeatable and static framework of directions, permitting the output from one operate to tell one other and the whole lot of the interplay to remain inside the boundaries of this system. This modality ought to align effectively with the prompt-completion mechanics in brokers like ChatGPT.
As an instance the method, let’s specify the parameters for a mini-app inside ChatGPT4 designed to operate as an Interactive Innovator’s Workshop. Our mini-app will incorporate the next features and options:
- Work on New Thought
- Broaden on Thought
- Summarize Thought
- Retrieve Concepts
- Proceed Engaged on Earlier Thought
- Token/”Reminiscence” Utilization Statistics
To be clear we won’t be asking ChatGPT to code the mini-app in any particular programming language and we are going to mirror this in our program parameters.