Glossary
Prompt Engineering
Prompt engineering is the practice of designing instructions and context that guide a language model toward a useful response. In AI search measurement, prompts should mirror real buyer questions rather than manipulate an answer, because the goal is to observe how an engine represents the market.
In plain terms
It is how a question is written for an AI system. Small wording changes can produce different answers.
Why it matters
Prompt design determines what a visibility study measures. Biased or leading prompts can make a brand appear stronger than it is.
How to apply it
- Use language collected from sales calls and search demand.
- Include category, problem, comparison and vendor prompts.
- Document every prompt exactly so later runs are comparable.
Example
A software company tests “best payroll software” and “payroll systems for a 200-person distributed company” separately because the added context changes which vendors qualify.
Sources
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Ranking is no longer enough
You need to be cited, mentioned, and recommended.
Being cited, mentioned, and recommended are three different outcomes, and most brands only ever achieve the first one. Ranking is no longer enough because AI engines answer buyers directly and name only a short list of vendors as the recommendation — everyone else is cited in passing, if at all. Get a free AI Visibility Report to see exactly where your brand appears today across ChatGPT, Google AI Overviews, Gemini, Perplexity, and Copilot, where competitors are winning the recommendation instead, and what's keeping you from moving up the shortlist.