Prompts for Public-Sector LLMs Should Be Governed as Commons

• URL: http://arxiv.org/abs/2606.00873v1
• Date: Sat, 30 May 2026 20:01:53 GMT
• Title: Prompts for Public-Sector LLMs Should Be Governed as Commons
• Abstract summary: We argue that prompts used to deploy large language models should be treated as governed artefacts rather than private, transient inputs.<n>We propose Prompt Commons: a versioned, community-maintained repository of prompt templates with provenance metadata, licensing, and moderation logs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper argues that prompts used to deploy large language models (LLMs) in public-sector settings should be treated as governed artefacts rather than private, transient inputs. Prompts encode role instructions, decision framings, and value claims; prompt choice can materially shift outputs even when model weights and input records are held fixed. Existing governance tools, including model and dataset documentation, organisation-level policies, and post-training alignment, rarely make the local prompt collections used in deployment transparent, contestable, or auditable. We propose Prompt Commons: a versioned, community-maintained repository of prompt templates with provenance metadata, licensing, and moderation logs. Using a pilot dataset collected with community partners in a large North American city (443 human prompts; 3,317 after augmentation), we illustrate three governance states (open, curated, veto-enabled) and a negotiation-oriented ensemble method that aggregates stakeholder prompts into compromise recommendations. We close with falsifiable implications and an evaluation agenda for prompt-layer governance.

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