What Is Prompt Engineering?
Prompt engineering is the practice of designing and refining the text inputs (prompts) given to AI language models to produce more accurate, relevant, and useful outputs. It encompasses techniques like providing clear context, specifying output format, giving examples (few-shot prompting), breaking complex tasks into steps (chain-of-thought), and defining the role or persona the AI should adopt. Prompt engineering matters for governance because the quality and safety of AI outputs depends significantly on how the AI is instructed — a poorly engineered prompt can produce biased, inaccurate, or harmful results from an otherwise capable model.
Why it matters for governance
From a governance perspective, prompt engineering creates several obligations. Prompt templates used in business processes should be reviewed for bias, tested for edge cases, and documented as part of the AI system's governance record. System prompts that set AI behaviour boundaries are safety-critical components that should be version-controlled and subject to change management. Organisations should establish prompt governance policies that define who can create and modify production prompts, how prompts are tested before deployment, and how prompt performance is monitored over time.