Prompt engineering is the skill of designing prompts so an AI model reliably produces what you need. It is less about clever tricks and more about clear communication: stating the task, the audience, the format and the limits.
Common techniques include giving the model a role, supplying a few worked examples, breaking a task into steps, specifying the output format (such as a table or short bullet list) and telling the model what to do when it is unsure. Teams then test prompts against a set of real questions and refine them, much like tuning a recipe.
In retail and hospitality, prompt engineering sits behind staff tools and customer assistants alike. A venue might refine a prompt so its booking assistant always confirms party size and date, quotes allergen information only from the approved menu, and hands over to a manager for complaints.
Pitfalls include fixing problems with ever longer prompts, which adds cost and can confuse the model, and testing on only a few easy examples. Prompts also need maintaining: when a model is updated or your menu changes, results can shift. Keep a library of tested prompts and a short checklist of questions to retest.
Good prompt engineering is a cheap way to lift quality and consistency before considering costlier options such as fine-tuning. It also makes AI behaviour easier to explain and audit.