Paper short abstract

Office workers learn LLM prompting not as a discrete skill but as a relational everyday practice, which complicates promised productivity gains. Their interactional work and practical reasoning diverge from official AI programs shaped by hierarchy, time, and access to AI tools and policies.

Paper long abstract

Drawing on in-depth interviews with employees of a large organization, this paper examines how workers learn to prompt LLMs and evaluate model outputs, attending to the gap between organizational prescriptions and workers’ practice.

The organization has invested in AI adoption infrastructures: internal platforms, usage guidelines, and teams tasked with spreading prompting knowledge. Prompting is institutionally framed as a learnable, discreet skill to be acquired through sanctioned channels (Korzyński et al. 2023; Liu et al. 2023). Yet many workers are unaware of these resources, do not use them or find them too generic.

Instead, workers construct their prompting competence relationally through peer exchange with colleagues whose domain expertise is situationally relevant, experimentation, and LLMs themselves as interlocutors for refining prompts (Mahdavi Goloujeh et al. 2024; Pakarinen & Huising 2025). Access to the time required is unevenly distributed. The widespread use of ChatGPT instead of the endorsed Copilot as “shadow AI” (Mansner 2025) further exemplifies the distance between prescribed and situated learning practices (Holton & Boyd 2021; Oudshoorn & Pinch 2003).

When evaluating outputs, workers rely on experience and repetition rather than on formal criteria: judgements draw on “experience from past outputs” and “a bit of feeling.” Prompts are rewritten iteratively when results fall short, and what works is discovered by experiment (Zamfirescu-Pereira et al. 2023). We argue that the practical reasoning involved in “learning how to prompt” is a relational accomplishment, shaped by organizational structures yet irreducible to them (Eyal & Pok 2011; Eyal, 2019).