After a successful event last week where we had the pleasure of hosting Giuseppe Scalamogna discussing his research on "Prompt Engineering Evolution: Defining the New Program Simulation Prompt Framework", we are thrilled to welcome him back for another insightful session. Towards a universal framework for prompt crafting in generative text models.

His research delves into the captivating realm of prompt engineering, which offers many ingenious techniques to shape the responses of AI models like ChatGPT. From Chain-of-Thought (CoT), Instruction-Based, N-shot, Few-shot, to unconventional methods like Flattery/Role Assignment - each method is a source of inspiration for creating a library of prompts that cater to various requirements. Bearing in mind the rapid pace of innovation in prompt engineering, there's a chance this method may already exist in some form. The focal point of this technique is to enable ChatGPT to function in a way that simulates a program, consisting of a sequence of instructions usually bundled into tasks. It acts as a fusion of Instruction-Based and Role-Based prompting methods, aspiring to use a static framework of instructions.

This approach allows the output of one function to affect the next, containing the entire interaction within the limits of the program. Resonating well with the prompt-completion mechanics within ChatGPT, this method promises considerable potential and aligns itself remarkably with real-world applications.