Paper short abstract:

In the context of Large Language Models (LLMs), I describe Participatory Prompting: a novel approach to the rising discipline of prompt engineering, that attempts to redistribute user agency and subvert popular deterministic narratives on technological omniscence and algorithmic fetishism.

Paper long abstract:

This contribution suits the suggested topics of distributed agency and participatory methods. In this abstract, I describe an empirical and methodological effort, which I am currently developing as part of my PhD project at the University of Edinburgh, to re-imagine and re-design the rising discipline of prompt engineering in Artificial Intelligence (AI). In the context of Large Language Models (LLMs), prompt engineering refers to finding the most appropriate input – or prompt – to allow the model to solve a particular task (Liu et al., 2023, p.1; White et al., 2023). Due to the capability of LLMs to generate, under certain conditions, novel textual instances that may appear humanlike, prompt engineering is often sensationalised through popular narratives on omniscience and algorithmic fetishism (Luitse and Dankena, 2021). Particularly relevant to the performativity of LLMs are sociotechnical accounts of computers as “thinking machines”, associated with promises of efficiency, rationality and objectivity (Alexander, 1990, p.162; Natale and Ballatore, 2020). Alexander (1990) draws a parallel between computational technologies and sacred entities, suggesting the existence of imagined associations between sophistication and awesomeness. In my work, I attempt to subvert deterministic narratives on prompt engineering and text generation through running workshops on what I refer to as Participatory Prompting. This effort observes the Design Justice framework, suggesting that individuals and communities directly affected by the functionality of technological artefacts should form stances on technology design (Costanza-Chock, 2020). In these workshops, participants discuss relevant dimensions of their identity and co-design values-oriented prompts for an open-source LLM.