Prompt engineering is the practice of designing, testing, and refining the instructions and information given to an AI system to help it perform a task. It can involve specifying the goal, providing relevant context, defining an output format, and evaluating whether the response meets the requirements.
It changes the input to a system, not the model's underlying training. Good prompts can reduce ambiguity, but cannot guarantee factual answers or remove the need for verification.
Practical relevance
For AI visibility research, wording can change which brands appear. "Which accounting tools suit a small nonprofit?" tests a different situation from "Explain why Brand X is the best accounting tool." The second prompt supplies the brand and encourages a favorable conclusion.
Use realistic customer questions, record the exact wording, and keep prompts consistent across comparisons. Test meaningful variations separately rather than adjusting prompts until a preferred brand appears.