Sustainability & AI
Prompt engineering
Designing the inputs given to generative AI systems to steer the quality and reliability of their outputs.
Definition
The practice of designing inputs — instructions, context, examples, constraints — to steer the behaviour and output quality of generative AI systems.
Quick reference
At a glance
- Subject
- Sustainability & AI
- Editorial status
- Editorial draft
- Definition status
- Established
- Last updated
- 21 August 2026
References
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This source supports the explanation of how the term is applied, measured or governed in practice.
Overview
What it means
Because large language models respond to natural-language instructions, the phrasing, structure and context of a prompt materially change the result. The practice has been systematised into documented patterns and techniques, surveyed in the academic literature since 2024.
How it is used
Practitioners use prompting to extract structured data from sustainability reports, control tone and citation behaviour in drafting tools, and constrain models to provided sources — often combined with retrieval rather than prompting alone.
Why it matters
Prompting is the cheapest governance lever over generative AI outputs, but also the most fragile: it guides rather than guarantees. Workflows that matter — disclosure, compliance, claims — need verification layers beyond well-crafted prompts.
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