Prompt engineering: how to get reliable answers from AI

Definition

Prompt engineering is the practice of designing and testing the instructions given to an AI model so that it produces accurate, consistent and useful results.

Beyond a single message

For casual use it means writing a clear prompt. For business use it goes further. You write a reusable instruction, test it against real examples, record where it fails and improve it. Typical techniques include giving the model a role, showing two or three examples of a good answer, stating the exact output format and telling it what to do when it does not know. That last step reduces hallucination. Everything must still fit in the context window.

It is closer to writing a good procedure for a new staff member than to coding. The people who know the work are usually better at it than the people who know the technology. Our guide to writing better prompts for work puts this into steps.

A hypothetical example

Imagine a property management agency that wants consistent replies to maintenance requests. They write one standard instruction that says to classify urgency, quote the relevant lease clause and never promise a repair date. They test it on twenty past requests, fix the cases it got wrong and then share the final version with the team.

Building these habits across a team is covered under AI adoption.