Definition
What this term means
The discipline of crafting effective inputs (prompts) to AI systems to elicit desired, accurate, and useful outputs. Prompt engineering involves understanding how AI models interpret instructions, what context improves response quality, and how to structure requests for optimal results. It applies to both end users writing queries and developers building AI-powered applications.
Why it matters
The business impact
Understanding prompt engineering reveals how your audience interacts with AI systems and what kinds of responses they receive. By analysing the prompts commonly used in your category (e.g., 'recommend a CRM for small businesses' vs. 'compare CRM features'), you can structure your content to align with the patterns that AI models expect to match. This is a form of reverse-engineering AI visibility from the user's perspective.
Used in context
How you might use this term
“A marketing team analysed 200 common prompts in their category to understand how users ask AI for recommendations. They discovered that prompts typically included budget constraints and team size. By adding structured content addressing these dimensions, their brand began appearing in AI responses to a much wider range of category prompts.”