An Outline for an Interrogative/Prompt Library to Help Improve Output Quality from Generative-AI Datasets

This chapter provides insight into an outline for what is termed a proposed ‘Interrogative/Prompt Library’ (IPL) designed to help optimise the quality of output when engaging with Generative-AI (Gen-AI) datasets, such as, most notably, the recently rapidly developing ChatGPT. We begin with a recap of the authors’ findings from earlier chapters. Next, the importance of questioning Large Language Model (LLM) datasets so that they can be better understood is covered, before investigating ‘interrogatives’ (involving who, why, what, when, where, how, etc. questions) and scoping their role in analytical search processes, including fundamentals relating to how interrogative questions are structured. Following on from the above work are ‘Phase 1’ suggestions towards building an ‘Interrogative Library Typology’, before delving more specifically into the area and activities of ‘Prompt Engineering’. The chapter then examines the development and maintenance of an Interrogative/Prompt Library, in the form of presenting a second phase. That work includes insight into the ‘Interrogative Prompt Library Engine’ that underpins the above work. A number of overall Conclusions and Key Takeaways are then tabled, noting especially the guidance value acquired from engaging with the activities discussed throughout the chapter. Thereby, end-users are increasingly better armed for engaging with Gen-AI datasets helping ensure that they best reduce the risks of, amongst others, falling into ‘Garbage In, Garbage Out’ (GIGO) traps. Finally, we end with a ‘call for action!’ for further research and development relating to what is tabled in the chapter paving the way for further collaboration. Appendices are also included to provide further reference detail.