A new implementation guide sets out a practical framework for using Large Language Models (LLMs) responsibly across the UK Civil Service.
Using Large Language Models Responsibly in the Civil Service is an 80-page document to provide senior officials and policy teams with a roadmap for deploying generative AI in ways that align with government standards, Civil Service values, and security protocols.
The guide, from the Bennett Institute for Public Policy at the University of Cambridge, complements the Cabinet Office’s Generative AI Framework for HMG, translating high-level principles into actionable steps for everyday use in policymaking, research, and administrative functions.
At a time when pressure is mounting to increase productivity and innovation across government, the guide argues for a risk-based, structured approach to AI adoption - one that enhances, rather than replaces, Civil Service expertise.
Bridging technology and policy
Author Aleksei Turobov, a Research Associate at Cambridge’s AI and Geopolitics Project, stresses the importance of aligning LLM usage with the Civil Service Code. “This guide is about equipping officials to harness AI’s capabilities without compromising public trust, data protection, or professional judgment,” he writes.
The framework introduces a tiered approach to AI implementation, with differentiated guidance for low-risk tasks such as summarising documents, and more stringent controls for higher-risk activities like policy formulation or implementation analysis. It outlines practical techniques for prompt engineering, output validation, and integrating AI into existing workflows while preserving transparency and accountability.
A structured approach to innovation
For senior leaders, the report provides a step-by-step roadmap for adoption, from pilot projects and capability assessments to organisation-wide scaling. It also recommends the creation of a centralised hub or Centre of Excellence to coordinate implementation, support departmental experiments, and manage cross-cutting risks.
The guide also draws clear boundaries around the types of data that should not be processed with commercial LLMs, including anything classified or containing sensitive personal information. It emphasises the use of approved platforms, audit trails, and role-based access controls as essential safeguards.
Rather than advocating for wholesale automation, the guide positions LLMs as collaborative tools to support - not supplant - Civil Servants. It provides templates for policy analysis, ministerial briefings, research synthesis, and more, with a strong emphasis on quality assurance and human oversight.
“This is not about replacing professional judgment, but about augmenting it,” Turobov notes. “By giving civil servants the tools and structure they need, we can responsibly integrate AI into decision-making while maintaining public service values.”
The full guide is available now via the Bennett Institute for Public Policy.