What it means
Prompt engineering is the practice of designing and testing prompts so that an AI model produces more useful, reliable and consistent outputs. It includes giving clear instructions, context, examples and output formats, and systematically comparing results.
Real-world example
An HR team wants an AI tool to draft first versions of job advertisements in inclusive language. They write a structured prompt with the role details, their style guide and two good example ads, then test it on 20 past vacancies and refine the instructions until reviewers consistently rate drafts as usable.
Why it matters for AI adoption
Good prompts can noticeably improve results without changing the model or adding data, which makes prompt engineering an accessible skill for non-technical staff. But prompts cannot make up for missing information or a model that is unsuitable for the task, and results should be tested on real cases rather than a few hand-picked ones.
Questions to ask
- How have we tested this prompt, and on how many real examples?
- Who owns and maintains the prompts used in our production systems?